International Journal of Statistics in Medical Research
The International Journal of Statistics in Medical Research seeks to publish new biostatistician models and methods, new statistical theory, as well as original applications of statistical methods, important practical problems arising from several areas of biostatistics and their applications in the field of public health, pharmacy, medicine, epidemiology, bio-informatics, computational biology, survival analysis, health informatics, biopharmaceutical etc.
Policy for Journals/Articles with Open Access Authors who publish with this journal agree to the following terms: Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work Policy for Journals / Manuscript with Paid Access Authors who publish with this journal agree to the following terms: Publisher retain copyright . Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work .
Sex Estimation in Terms of Inclination and Alsberg in Proximal Femur by using Machine Learning Algorithms
The fact that the femur has a solid structure ensures that the integrity of the bone is preserved, making it favorable in the sex determination process. In this study, it was aimed to predict sex by using angular variables of femur from computed tomography (CT) images and machine learning algorithms. In this study, a total of 4 angular measurements, including the femoral inclination angle (FIA) and Alsberg angle (FAA) of the proximal femur on both sides, were evaluated on CT images of 88 female and 92 male adults. Logistic regression (LR) and classification and regression tree (CART) machine learning algorithms were used for sex diagnosis. 5-fold cross-validation method was used in the training and testing processes of the models. Model performances were evaluated with area under the ROC curve, Precision and Recall statistics. Of the 4 angle measurements evaluated, only the right side FIA mean was significantly higher in women (p=0.042). The sex diagnosis success of the LR model and the CART algorithm were found to be similar (p values 0.014 and 0.017, respectively). When the success criteria of each algorithm were examined, we saw that although sex estimation was significant, (Acc 0.61, Acc 0.60, respectively) they were not very successful. We found that the machine learning algorithms applied to the variables of proximal femur angle parameters gave low accuracy of sex and the effect of both models on sex estimation was similar.
Special Issue: Trends in Artificial Intelligence and Machine Learning in Healthcare
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Determination of Alzheimer's Disease Stages by Artificial Learning Algorithms
Introduction: This study aims to determine the stages of Alzheimer's disease (AD) using different machine learning algorithms, and compares the performance of these models. Methods: Demographic, genetic, and neurocognitive inventory data from the National Alzheimer's Coordinating Center (NACC) database as well as brain volume/thickness data from magnetic resonance imaging (MRI) scans were used. Deep Neural Networks, Ordinal Logistic Regression, Random Forest, Gaussian Naive Bayes, XGBoost, and LightGBM models were used to identify four different ordinal stages of AD. Results: Although the performance measures of the developed models were similar, the highest classification rate of AD stages was achieved by the Random Forest model (accuracy: 0.86; F1 score: 0.86; AUC: 0.95). The outputs of the model with the best performance were explained by the SHapley Addictive exPlanations (SHAP) method. Conclusions: This indicates that non-invasive markers and machine learning models can be used effectively in early diagnosis and decision support systems to predict stages of AD.
National Alzheimer's Coordinating Center
A Flexible Extension of the Log-Logistic Distribution with Application to Cancer Data
This article introduces the Type II Half Logistic Topp-Leone-G (TIIHLTL-G) family, which unifies the structural properties of the Type II Half Logistic-(G TIIHL-G) and Topp-Leone-G (TL-G) family of distributions. The novelty of the TIIHLTL-G family lies in its enhanced shape flexibility and ability to model various skewness and kurtosis patterns beyondthose captured by existing extensions. The statistical features of the new TIIHLTL-G family have been thoroughly investigated, including the probability-weighted moment, hazard function, moments, order statistics, quantile function, and survival function.Parameters are estimated using classical techniques, with maximum likelihood estimation performing best overall. Application to two real cancer datasets demonstrates the superiority of the proposed model over competing distributions, including the Log-logistic and related variants, with lower AIC, and BIC confirming its improved goodness-of-fit and predictive accuracy.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
Development and Validation of a Brief Instrument to Evaluate Primary-Care AMI Management in Mexico
This descriptive cross-sectional study developed and validated an instrument to evaluate the initial management of acute myocardial infarction (AMI) at the primary-care level in Mexico. The instrument was constructed from the Mexican Social Security Institute Infarction Code and the national Clinical Practice Guideline, extracting core elements for first-contact AMI care. Expert judgment guided item selection using the Rovinelli and Hambleton approach, and items with Aiken’s index ≥0.70 were retained. A pilot test with 35 primary-care physicians assessed the preliminary version. The field sample comprised 143 physicians from the 17 municipalities of Tabasco, selected by convenience sampling. Reliability was estimated with Cronbach’s alpha. The pilot version showed α=0.636; after expert validation and refinement—including the addition of two items (on fibrinolytic dosing in adults ≥75 years and post-fibrinolysis protocol)—the final 10-item instrument achieved α=0.817. Corrected item–total correlations improved notably for item 2 (from 0.243 to 0.544), while items 5 and 8 showed the highest values in the final version. Factorability was adequate (KMO = 0.736; Bartlett’s χ²(36) = 83.609, p < 0.001). This brief, context-specific tool shows solid internal consistency and expert-supported content validity for primary-care AMI management; structural and criterion (predictive) validity should be further confirmed.
Robustness of Bayesian Methods in Healthcare System Assessment: A Comprehensive Review
Background:Healthcare systems generate heterogeneous, incomplete, and evolving data; methods that combine prior knowledge with new evidence are needed. Aim:The present research critically evaluates the usefulness and resilience of Bayesian methods for healthcare system assessment. Scope:This study synthesizes foundational principles and contrasts with frequentist approaches; examines applications across quality of care benchmarking, health economic evaluation, epidemiologic surveillance, resource allocation, policy appraisal, and personalized medicine; and highlights computational advances enabling practical deployment. Key Findings:Bayesian techniques provide partial pooling through hierarchical models, formal incorporation of prior information, accurate probabilistic inference, and dynamic updating as data accumulates. These features give more stable estimates in sparse settings, transparent quantification of uncertainty, and decision‑relevant outputs (e.g., posterior probabilities and cost-effectiveness acceptability). Modern samplers and approximate inference make complex models tractable, yet results remain sensitive to prior specification and data quality, stressing the need for validation, sensitivity analysis, and clear reporting. Conclusion:Bayesian methods offer a meticulous, flexible framework for assessing performance, value, and equity in healthcare systems. They can enhance policy-making and clinical decision support when paired with principled prior elicitation, robust computation, and reproducible workflows. Next, the practical recommendations and research priorities to accelerate responsible adoption across healthcare analytics were outlined. At the end, this review highlights both methodological robustness and translational potential, positioning Bayesian methods as indispensable for evidence-based healthcare decision-making
Automated Detection of Posterior Tibial Slope on X-Ray Images Using VGG19
Elderly and overweight individuals are particularly vulnerable to developing muscle weakness and joint pain as a result of osteoarthritis (OA). This degenerative joint condition often affects the ligaments and primarily damages the cartilage. Healthy cartilage, being smooth and elastic, enables bones to glide effortlessly over one another, providing stability and preventing friction between bone surfaces. When this protective tissue deteriorates partially or completely, it results in painful stiffness and discomfort caused by direct bone contact. The diagnosis of osteoarthritis typically involves a combination of clinical assessment and diagnostic imaging techniques such as X-rays or MRI scans. The present study focuses on utilising advanced image-based feature extraction methods for the identification and classification of knee osteoarthritis. This approach aims to enhance diagnostic accuracy by improving the differentiation of structural changes observed in medical images
Machine Learning-Based Prediction of Seasonal Influenza Trends in Saudi Arabia: A Tool for Regional Public Health Planning
Influenza has continued to be a worldwide social health problem, especially in high-density population areas with minimal early-warning mechanisms. The present study evaluates the predictive ability of two machine learning models Support Vector Regression (SVR) and Random Forest (RF) to predict weekly influenza cases in Saudi Arabia, spanning from 2017 to 2022, on 313 weekly influenza records in the WHO Global Influenza Surveillance and Response System (GISRS). The performance of these models was measured with R², MAE, MSE, and RMSE. Although SVR had a better training accuracy (R² = 0.96), RF had a better generalization (R² = 0.818) and more consistent predictions at the peaks of the seasons. These observations show that RF is appropriate to real-time influenza surveillance and can provide a replicable and versatile framework to assist data-driven epidemic preparedness in Saudi Arabia and other similar contexts throughout MENA and Asia-Pacific.
Bilal-G Family of Distributions with Applications to Biomedical and Reliability Engineering Data
This paper introduces the Bilal-G (B-G) family of distributions, a novel generator-based method for enhancing the flexibility of existing probability models to better accommodate complex data structures prevalent in biomedical and reliability engineering. Data from these fields frequently exhibit features like high skewness, significant outliers, and non-monotone hazard rates that challenge conventional distributions. Using the Bilal distribution as the generator, we construct the new family’s general cumulative distribution function (CDF) and probability density function (PDF), from which a key, parsimonious sub-model, the two-parameter Bilal-Exponential (BE) distribution, is derived. We thoroughly analyze the BE distribution’s properties, including its capability to model an increasing hazard rate, which is supported by Total Time on Test (TTT) plots of the application datasets. A comprehensive simulation study evaluates the performance of fifteen distinct non-Bayesian estimators, revealing that the Minimum Spacing Linex Distance (MSLNDE) method consistently provides the most accurate and precise parameter estimates across various sample sizes. Finally, the superiority of the BE distribution is demonstrated through its successful application to two real datasets: one on patient mortality rates and one on component failure times. For the mortality data (Data I), the BE model reduced the Akaike Information Criterion (AIC) by 1.99 units compared to the classical Weibull distribution. For the component failure data (Data II), the Bayesian Information Criterion (BIC) was reduced by 0.41 units compared to the best-fitting competing model (TIHTE), confirming the BE distribution’s exceptional goodness-of-fit and reliability as a practical lifetime model.
Gender Prediction from Angular and Linear Parameters in Cranium Lateral View by using Machine Learning Algorithms: A Computed Tomography Study
The purpose of the study was to predict a gender by using Machine Learning Algorithms (MLA) with variables of the lateral view of the cranium from Computed Tomography (CT) images. A total of 5 parameters (3 linear and 2 angular) of the lateral view of the cranium were evaluated on CT images of 200 female and 200 male adult individuals in the present study. These parameter measurements were analyzed with MLA and Logistic Regression (LR), Random Forest (RF), Linear Discriminant Analysis (LDA), K-Nearest Neighborhood (KNN) and Naive Bayes (NB) models were used. Accuracy (Acc), Sensitivity (Sen), Specificity (Spe) and F1 scores (F1) were used as the evaluation criteria in the study. As a result of MLA, the Acc ratio was found to be 0.77 for the KNN algorithm, 0.84 in the NB algorithm, 0.85 in the LDA algorithm, 0.70 in the RF algorithm and 0.81 in the LR algorithm. As a result of the analysis, 0.85 Acc, 0.85 Sen, Spe 0.85 and 0.85 F1 values were found in the LDA algorithm with the highest accuracy. When the significance level of the variables in the study was examined, it was found that variable A had the best effect. It was found that the MLA used for the variables of the lateral view of the cranium yielded high accuracy regarding gender and the LDA Model was effective in predicting gender.
Heart Disease Prediction using an Ensemble Learning Method: A Study at King Abdullah Hospital in Bisha, Saudi Arabia
The detection of diseases is essential to improving healthcare outcomes and saving lives. Thanks to technological advancements in medicine, machine learning has become a valuable tool for predicting future patient health outcomes. Despite the abundance of available patient data, accurately predicting cardiac disease has become increasingly challenging. In response, we developed an innovative ensemble learning approach (ELA) that combines three powerful machine learning (ML) techniques. Our ELA provides reliable predictions of cardiac disease that surpass those of the individual classification algorithms, resulting in higher accuracy. Our research yields a new combination of classification algorithms that significantly increases the prediction accuracy. We tested our model on a regional dataset collected from King Abdullah Hospital in Bisha, Saudi Arabia. We obtained the best results false negatives (FN ) of 8, true positives (TP) of 70, true negatives (TN) of 72, false positives (FP) of 6, accuracy of 0.9113, sensitivity of 0.8839, specificity of 0.95, PPV of 0.9389, NPV of 0.8878, AUC of 0.9569, F1 of 0.9133 Kappa of 0.8220, MCC of 0.8277 with an ELA comprising logistic regression (LR), extra trees (ET) and support vector machine (SVM) with radial basis function (RBF) kernel. With our ELA, medical professionals can detect cardiac disease and provide timely interventions to prevent potentially life-threatening health issues.
Machine Learning-Based Maternal Health Risk Assessment: A Comparative Analysis of Classification Algorithms for Predicting Risk Levels During Pregnancy
Background: Maternal health risk assessment remains a critical challenge in healthcare, particularly in resource-limited settings where early identification of high-risk pregnancies can significantly impact maternal and fetal outcomes. This study evaluates the performance of multiple machine learning algorithms for predicting maternal health risk levels using physiological parameters. Methods: We analyzed a dataset of 1014 pregnant women from Kaggle, incorporating six key features: age, systolic blood pressure, diastolic blood pressure, blood sugar levels, body temperature, and heart rate. Risk levels were classified as mild (0), moderate (1), and severe (2). Four machine learning algorithms were implemented and compared: Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Results: Random Forest and SVM achieved perfect classification performance with 100% accuracy, precision, recall, and F1-scores across all risk categories. Logistic Regression demonstrated strong performance with 98% overall accuracy, showing minor challenges in recall for moderate risk cases (93%). KNN achieved 98% accuracy with balanced performance across risk categories, though slightly lower precision for mild risk cases (95%). Conclusion: Machine learning algorithms, including Random Forest and SVM, show promise in predicting maternal health risks; however, further validation across diverse populations is essential before clinical adoption.
Transforming Breast Cancer Prediction: Advanced Machine Learning Models for Accurate Prediction and Personalized Care
Background: Breast cancer is the most common malignancy among women worldwide, underscoring the importance of early detection and accurate prognostication. Machine learning (ML) has emerged as a promising approach, offering powerful tools for analyzing complex datasets in breast cancer prediction and diagnosis. Objective: This study evaluates the predictive performance of diverse ML algorithms for breast cancer classification using publicly available datasets, focusing on accuracy, interpretability, and generalizability. Methods: The dataset included clinical and demographic variables such as age, menopausal status, tumor size, and lymph node involvement. Data preprocessing addressed missing values and class imbalance, with the Synthetic Minority Oversampling Technique (SMOTE) applied to improve sensitivity for the minority class. Feature engineering involved interaction terms and scaling of numerical variables. Multiple ML models—Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), and Neural Networks—were trained and evaluated. Performance was measured using sensitivity, F1-score, and AUC-ROC. Model interpretability was enhanced with SHapley Additive exPlanations (SHAP). Results: Random Forest achieved the best performance with an AUC-ROC of 0.9751, followed by Gradient Boosting (0.9242) and Neural Networks (0.9254). Logistic Regression and SVM yielded comparable results (0.9005 and 0.9344). Ensemble models showed higher accuracy and generalizability, particularly on external validation. Tumor size and lymph node involvement emerged as key predictors. SMOTE improved sensitivity across models. Conclusion: This study demonstrates the potential of ML in breast cancer prediction, emphasizing the effectiveness of ensemble methods and interpretability tools. Future work should focus on integrating ML into clinical practice for earlier detection and personalized treatment.
Evaluation of A Novel Risk Factor Screening Tool for Gestational Diabetes Mellitus: A Machine Learning Based Predictive Method
Introduction: Gestational diabetes mellitus (GDM) is a significant pregnancy complication linked to adverse outcomes for both mother and child. Early identification of high-risk individuals is crucial for effective management and prevention for the onset/progression of the GDM. Our study aims to a) evaluate the effectiveness of a newly developed machine learning based risk factor screening tool for predicting GDM and b) to compare its predictive performance against established models and current literature. Methods: This study explored SNP data from the leptin (LEP) and leptin receptor (LEPR) genes to develop machine learning models for predicting gestational diabetes mellitus (GDM). It included data preprocessing, such as cleaning and feature selection, focusing on genetic markers, metabolic parameters, and demographic information. Various algorithms, including Logistic Regression, Decision Trees, and Random Forests, were used, and their performance was evaluated using metrics like accuracy and ROC-AUC to determine the best model for GDM prediction. Results: The newly developed screening tool demonstrated a sensitivity of 85%, specificity of 78%, positive predictive value (PPV) of 68%, and negative predictive value (NPV) of 90% in predicting GDM. Comparatively, machine learning models showed higher sensitivity (90-95%) but lower specificity (65-75%). Conclusion: The developed risk factor screening tool is a viable method for predicting GDM, with accuracy metrics comparable to advanced machine learning models and established literature. Future research should focus on refining these tools and exploring their integration into routine prenatal care to enhance early detection and intervention strategies for GDM.
Comparative Analysis of Machine Learning Models for Early Heart Disease Diagnosis
Heart disease remains among the leading causes of death worldwide, and its early detection ability can be the difference between life and death. In this research, we investigate the capability of machine learning—namely Support Vector Machines (SVM)—to predict the occurrence of heart disease based on regular clinical information. We used the Cleveland Heart Disease dataset, which contains critical patient data like age, gender, blood pressure, cholesterol level, type of chest pain, and other crucial health factors. Prior to creating our model, we pre-processed and cleaned the data by dealing with missing values, changing categorical variables into numerical form, and scaling the features for uniformity. We then optimized the SVM model using grid search and cross-validation to make it run at its optimal level. The resulting model had an accuracy of 86.41% in the test set and performed better than other popular models such as logistic regression and random forest. The significant about this work is the potential for applying it in practical situations. An SVM-based program such as this could be a second opinion for physicians or integrated into early diagnostic tools—most helpful in clinics with limited access to specialists. It's progress toward smarter, data-driven healthcare that enables faster and more precise diagnoses. There's still potential for expansion, using bigger, more varied datasets or incorporating real-time patient information could further enhance the model. But this research demonstrates that with the proper data and methodology, machine learning can be a useful tool in the early diagnosis of heart disease.
A Novel Method for Viral Conjunctivitis Detection using CNN-Based Image Analysis
Viral conjunctivitis, also known as “Eye Flu,” presents significant public health challenges worldwide. India has recently witnessed a surge in cases, affecting numerous people and causing widespread concerns. This research delves into the realm of medical image processing and deep learning to address the pressing need for accurate and efficient detection of viral conjunctivitis, a highly contagious ocular infection. Leveraging advancements in computer vision and convolutional neural networks (CNNs), the study focuses on the development and evaluation of a robust diagnostic system capable of discerning viral conjunctivitis from other common forms of conjunctivitis, namely allergic and bacterial. Challenges in using image processing for disease detection include the need for large amounts of descriptive data to train machine learning models, ensuring the accuracy and reliability of image analysis algorithms, and addressing data privacy and security concerns. Future directions in this field may include developing more advanced deep learning models that can handle complex medical imaging data, integration of imaging technology and other diagnostic tools to diagnose diseases and learn how to use it in real-time for fast processing. and more efficient diagnosis. Additionally, efforts should be made to standardize image processing protocols in different healthcare settings to facilitate sharing and comparison of medical imaging data for research and clinical purposes. Overall, more research and collaboration between medical, informatics, and image processing experts is essential in developing future image processing applications for disease detection. The methodology encompasses the acquisition of a diverse dataset comprising annotated images of ocular conditions, rigorous preprocessing techniques to standardize image quality, and the implementation of three distinct CNN architectures: ResNet, VGG, and GoogleNet. These architectures were selected for their proven efficacy in medical image analysis and classification tasks. Through extensive experimentation and rigorous validation, the research elucidates the efficacy of each architecture in accurately classifying conjunctival diseases, with a particular emphasis on delivering actionable diagnostic outcomes.
AI-Powered CNN Model for Automated Lung Cancer Diagnosis in Medical Imaging
Lung cancer remains a critical health concern in the entire world, which has been a major cause of high rates of cancer-related mortalities that affect individuals in every part of the world. The findings emphasize the notable potential of deep learning procedures to assist radiologists in diagnosing cases of lung-related abnormalities appropriately. Such methods are also leading to the improvement of AI-based healthcare products. The enhancements to the suggested model [16, 17, 18, 21] in the future will be aimed at tuning hyperparameters, 3D CNN [16, 17, 18] architectures, and the integration of patient clinical data, with the aim of further increasing the accuracy [16, 17, 19] of diagnosis as well as system performance. This paper uses the IQ-OTHNCCD dataset, a publicly available and highly annotated set of CT imaging that has been annotated by experts in the medical field. The preprocessing techniques applied will involve changing the images to Grayscale, normalizing the pixel values, ensuring consistency in the images, and converting them to a standard size of 128x128 pixels, which is the ideal size to feed the images into the CNN [16, 17, 18]. In the proposed work, the model [16, 17, 18, 21] integrates multi-scale convolutional layers with adaptive dropout (rate=0.5) and ReLU activations, yielding 95% accuracy [16, 17, 19] and 0.95 F1-score (95% CI: 93.8–96.2%) on a 70/15/15 train/validation/test split— a 4% improvement in F1-score. Preprocessing includes grayscale conversion, pixel normalization to [0,1], and resizing to 128x128 pixels. The architecture comprises three convolutional blocks (32/64/128 filters, 3x3 kernels), max-pooling (2x2), flattening, a 512-unit dense layer, and a 3-unit softmax output. Future enhancements include hyperparameter tuning, 3D CNN [16, 17, 18] integration, and clinical data fusion to exceed 97% accuracy [16, 17, 19].
Multiple Mean Comparison for Clusters of Gene Expression Data through the t-SNE Plot and PCA Dimension Reduction
This paper introduces a novel methodology for multiple mean comparison of clusters identified in gene expression data through the t-distributed Stochastic Neighbor Embedding (t-SNE) plot, which is a powerful dimensionality re- duction technique for visualizing high-dimensional gene expression data. Our approach integrates the t-SNE visualization with rigorous statistical testing to validate the differences between identified clusters, bridging the gap between exploratory and confirmatory data analysis. We applied our methodology to two real-world gene expression datasets for which the t-SNE plots provided clear separation of clusters corresponding to different expression levels. Our findings underscore the value of combining the t-SNE visualization with multiple mean comparison in gene expression analysis. This integrated approach enhances the interpretability of complex data and provides a robust statistical framework for validating observed patterns. While the classical MANOVA method can be applied to the same multiple mean comparison, it requires a larger total sample size than the data dimension and mostly relies on an asymptotic null distribution. The proposed approach in this paper has broad applicability in the case of high dimension with small sample sizes and an exact null distribution of the test statistic. Objective: Propose a two-step approach to analysis of gene expression data. Gene expression data usually possess a complicated nonlinear structure that cannot be visualized under simple linear dimension reduction like the principal component analysis (PCA) method. We propose to employ the existing t-SNE approach to dimension reduction first so that clusters among data can be clearly visualized and then multiple mean comparison methods can be further employed to carry out statistical inference. We propose the PCA-type projected exact F-test for multiple mean comparison among the clusters. It is superior to the classical MANOVA method in the case of high dimension and relatively large number of clusters. Results: Based on a simple Monte Carlo study on a comparison between the projected F-test and the classical MANOVA Wilks’ Lambda-test and an illustration of two real datasets, we show that the projected F-test has better empirical power performance than the classical Wilks’ Lambda-test. After applying the t-SNE plot to real gene expression data, one can visualize the clear cluster structure. The projected F-test further enhances the interpretability of the t-SNE plot, validating the significant differences among the visualized clusters. Conclusion: Our findings suggest that the combination of the t-SNE visualization and multiple mean comparison through the PCA-projected exact F-test is a valuable tool for gene expression analysis. It not only enhances the interpretability of high-dimensional data but also provides a rigorous statistical framework for validating the observed patterns.
The Effect of Interpersonal Communication on Prevention Behavior of Early Hypertension among Student at SMAN 6 and SMAN 19 Bone
Background: Hypertension is a health issue that is not only experienced by adults but can also develop during adolescence. This condition often continues into adulthood, with essential hypertension in adults frequently stemming from habits and risk factors that emerge during adolescence. Centers for Disease Control and Prevention (CDC) 2023 revealed that one in every 25 adolescents aged between 12 to 19 years old is diagnosed with hypertension. Among adolescents diagnosed with hypertension, 10% were found to have a prior history of prehypertension. Objective: This study aims to determine the effect of interpersonal communication on early hypertension prevention behavior among students of SMAN 6 and SMAN 19 Bone. Materials and Methods: The research design used was Quasi Experiment with pretest-posttest control group design. 110 grade 11 students made up the study population. They were split into two groups: the experimental group, which got an interpersonal communication intervention (n=55), and the control group, which received counseling (n=55). This study was carried out at SMAN 6 and SMAN 19 Bone. Simple random sampling was the method of sampling employed in this study, and a questionnaire was utilized as the research tool to gauge students' knowledge, attitudes, and action both before and after they received the intervention, which had been validated and proven to be reliable. Wilcoxon and Mann-Whitney tests were used for both univariate and bivariate data analysis. Results: This study showed significant differences in knowledge, attitudes, and actions in the experimental group regarding hypertension prevention behaviors, with p-values for knowledge (p=0.017), attitude (p=0.000), and action (p=0.002). Conclusion: The interpersonal communication approach applied in the intervention proved to have an influence on hypertension prevention behavior, including knowledge, attitudes, and actions in students of SMAN 6 and SMAN 19 Bone.
Assessing the Impact of Human and Technological Factors on Hospital Management Information System Utilization: A Case Study at Hospital X In Padang City Indonesia
This study examines the application of the HOT-Fit method, which evaluates the relationship between Human, Technology, and Net Benefit components within the Hospital Management Information System (HMIS) at Hospital X Padang. The Human component is assessed based on system usage and user satisfaction, while the Technology component is analyzed through information quality, service quality, and system quality. This study employs a quantitative crosssectional design, with the research population comprising all active users of the HMIS application at Hospital X Padang, including employees from various departments interacting with the system. The research aims to determine the extent to which the Human and Technology components influence the Net Benefit of the HMIS and to explore these relationships in greater depth. The findings reveal a significant relationship between the Human component and the Net Benefit, as well as between the Technology component and the Net Benefit of the HMIS. Among the factors examined, Technology emerges as the most dominant factor affecting the Net Benefit of the system. These results provide valuable insights for optimizing the implementation and impact of HMIS in healthcare settings.
Hospital Management Information System (HMIS)
Comparative Analysis of Kolmogorov-Inspired CNN and Traditional CNN Models for Pneumonia Detection: A Study on Chest CT Images
Aim: In this study, our goal is to compare the effectiveness of Kolmogorov Inspired Convolutional Neural Networks (KAN) with traditional Convolutional Neural Networks (CNN) models in pneumonia detection and to contribute to the development of more efficient and accurate diagnostic tools in the field of medical imaging. Methods: Both models are structured with the same layers and hyperparameters to ensure a fair comparison of their performance. For a robust evaluation, the relevant dataset was divided into 80% for training and 20% for testing. Results and Conclusion: Performance metrics of KAN; 95.2% sensitivity, 97.6% specificity, 94.1% precision, 96.9% accuracy (Acc), 0.9466 F1 score (F1) and 0. 9251 Matthews Correlation Coefficient (MCC), while the CNN model was found 92.5%, 96.4%, 91.2%, 95.3%, 0.9188 and 0.8858 for the same criteria, indicating that KAN outperformed. This comparison emphasizes that KAN has the potential to be a more effective model for pneumonia detection in chest CT images.
Kolmogorov-Inspired Convolutional Neural Networks
Predictors of Type-2 Diabetes Self-Screening: The Impact of Health Beliefs Model, Knowledge, and Demographics
Background: Diabetes mellitus (DM) is a global health concern, and the intention to undergo diabetes self-screening among patients varies based on demographics and the Health Belief Model (HBM). Objective: This study aimed to identify the factors associated with the intention to engage in DM self-screening. Methods: This study included 404 participants with a 99% response rate. Saudi Arabian residents from the Jazan region, all diagnosed with type 2 diabetes, were enrolled. A validated, Arabic-translated, and structured questionnaire was used to collect data on demographics, family history, chronic disease status, DM knowledge, HBM constructs, and DM screening behavior. The study methods adhered to the STROBE Checklist for clear and reliable reporting. Results: The study found that 24.5% of the participants were in the 35-44 age group and 67.3% were male. Regarding education, 52.2% had university-level education and 79.7% had no family history of DM. Among the participants, 62.1% reported no chronic disease. The mean knowledge score was 6.44 (SD = 2.01). The study revealed that 56.9% of the respondents intended to engage in DM screening. Factors associated with intention included age (65 and over had lower odds), gender (females had slightly higher odds), and education (school qualification had higher odds). Family history and chronic disease status did not significantly affect intention. Among the HBM constructs, higher perceived susceptibility increased the odds, higher perceived severity decreased the odds, and perceived benefits and barriers had no significant associations with intention. Conclusions: This study provides valuable insights into the factors influencing the intention to engage in DM self-screening among diabetic patients. This understanding can guide targeted interventions to promote DM self-screening and enhance diabetes care outcomes.
Prevalence of Depression among Women Using Hormonal Contraceptive Use: Insights from a Hospital-Based Cross-Sectional Study
Background: Hormonal contraceptives (HC) serve as a key component in managing premenopausal symptoms and controlling birth rates. However, mood-related side effects, ranging from minor disturbances to severe clinical depression, are the primary reasons for discontinuation. Objective: To assess the prevalence of depression among women who use hormonal contraceptive methods. Additionally, the study aims to explore the association between specific types of contraceptives—such as oral pills, implants, injectables—and the prevalence of depression. Methods: From October 2023 to October 2024, a total of 1500 women between the ages of 21 and 45 who currently take hormonal contraception participated in this hospital-based cross-sectional study, which was carried out at the tertiary care hospital at King Fahd Central Hospital's outpatient gynecology clinic. Results: The most frequent age categories were from 26 to 40 years (85.7%). The majority of the studied cases were non-lean (82.6%). Most of the cases had parity from 1 to 4 (97.1%). Women were mainly of a low social class (77.1%). Social problems were found in (21.8%). Hypertension and diabetes mellitus were in 4.9% and 3.2% respectively. The most frequent contraceptive method were OCPs (40.3%), followed by POPs (31.2%), then subdermal implants (16.3%), injectable (8.6%), hormonal IUD (2.2%) and patches (1.4%). Most of the studied women used such method from 3 to 6 years (88.2%). Prevalence of depression among the studied cases was (8.7%; CI: 7.3%–10.2%). Obese individuals demonstrated a significantly higher prevalence of depression (11.5%) compared to overweight (8.5%) and lean individuals (5.0%), with a statistically significant association (p=0.015). Additionally, obese participants were more likely to have diabetes mellitus (27.1%), face social issues (21.8%), and belong to a low socioeconomic class (77.1%). Regarding contraceptive types, depression was notably less common among women using combined oral contraceptives (COCs) and progesterone-only pills (POPs), with rates of 4.6% and 4.5%, respectively. In contrast, higher rates of depression were observed in users of subdermal implants (19.2%), injectables (18.6%), hormonal IUDs (18.2%), and hormonal patches (19.0%) (p<0.001). The duration of contraceptive use also played a significant role, with depression rates increasing progressively from 2.8% for women using contraceptives for 1–2 years to 3.7% for 3–4 years and 12.7% for 5–6 years. The highest rate of depression, 37.7%, was observed among women using hormonal contraceptives for seven or more years (p<0.001) Conclusion: Given the observed associations between certain hormonal contraceptives, prolonged use, and elevated depression rates, clinicians should adopt a proactive approach in assessing patients’ mental well-being, especially for women with additional risk factors like high BMI, socioeconomic challenges, or chronic conditions such as diabetes. Screening tools like the PHQ-9 should be routinely used during consultations to monitor for early signs of depression, allowing for timely intervention if needed.
Understanding Thrombocytopenia in the Obstetric Population: A Study from a Tertiary Care Center
Background: Thrombocytopenia in pregnancy is a common condition with diverse etiologies, ranging from benign causes such as gestational thrombocytopenia (GT) to more serious conditions like preeclampsia and immune thrombocytopenic purpura (ITP). The clinical implications of thrombocytopenia during pregnancy include potential maternal and fetal complications, highlighting the importance of early detection and appropriate management. Objective: To evaluate the incidence, causes, clinical outcomes, and complications of thrombocytopenia in pregnancy at a tertiary care hospital. Methods: This retrospective cohort study included 130 pregnant women who were diagnosed with thrombocytopenia during their antenatal care between 2020 and 2021. Data on demographics, etiology, severity of thrombocytopenia, and maternal and fetal outcomes were collected and analyzed. Results: The incidence of thrombocytopenia in pregnancy was found to be 3.85%. The most common causes were gestational thrombocytopenia (48.48%), preeclampsia (18.18%), and anemia (27.27%). Mild thrombocytopenia (<100,000/µL) was the most frequent severity (68.18%), with severe thrombocytopenia (<50,000/µL) observed in 6.06% of cases. Maternal complications included postpartum hemorrhage (10.60%) and incision site oozing (7.57%). Fetal outcomes included intrauterine growth restriction (12.12%) and birth asphyxia (7.57%). Most cases were diagnosed in the second trimester, and a significant proportion (56.06%) were in primigravida women. Conclusion: Thrombocytopenia in pregnancy is predominantly mild, with gestational thrombocytopenia being the most common cause. Although the condition generally carries a good prognosis, associated complications such as postpartum hemorrhage and adverse fetal outcomes underscore the need for careful monitoring. Early diagnosis and individualized management are essential to minimize risks for both mother and child.
A Conceptual Model of Sustainable Technology Use: The Role of Confirmation and Perceived Usefulness in the Hospital X Management Information System in Padang
Background and Objective: The adoption and use of Management Information Systems (MIS) in healthcare settings, like Hospital X in Padang, are crucial for improving operational efficiencies and patient care. Task-Technology Fit (TTF) measures how well technology supports its intended tasks and significantly influences user satisfaction and system use continuity. Key factors include Confirmation, assessing post-adoption user expectations, and Perceived Usefulness (PU), evaluating job performance enhancement. This study explores TTF's impact on Continuance Intention (CI), mediated by Confirmation and PU, within Hospital X's MIS context. Methods: Data were gathered from staff at H.B. Saanin Mental Hospital, one of West Sumatera's public hospitals. A total of 158 questionnaires were distributed, with 150 deemed analyzable using structural equation modeling. Result: The study finds no statistically significant relationship between TTF and PU. However, a marginally significant relationship between TTF and Confirmation suggests modest evidence that alignment between tasks and technology influences users' confirmation of their expectations. Notably, PU does not directly impact CI within Hospital X's MIS, nor does Confirmation significantly affect users' intention to continue using the system. Overall, the direct influence of technology-task alignment on users' intention to continue using MIS is inconclusive in this study context. Conclution: This study reveals complex relationships among TTF, Confirmation, PU, and CI within Hospital X's MIS framework. Despite the theoretical significance of TTF and Confirmation, their direct impacts on PU and users' intention to continue system use are not statistically significant. These findings emphasize the ongoing need to evaluate and adapt MIS strategies to better align with user needs and ensure sustained effectiveness in healthcare operations.
Optimizing Sample Size for Accelerated Failure Time Model in Progressive Type-II Censoring through Rank Set Sampling
Survival data is a type of data that measures the time from a defined starting point until the occurrence of a particular event, such as time to death from small cell lung cancer after diagnosis, Length of time in remission for leukemia patients, Length of stay (i.e., time until discharge) in hospital after surgery. The accelerated failure time (AFT) models are popular linear models for analyzing survival data. It provides a linear relationship between the log of the failure time and covariates that affect the expected failure time by contracting or expanding the time scale. This paper examines the performance of the Rank Set Sampling (RSS) on the AFT models for Progressive Type-II censoring-survival data. The Ranked Set Sampling (RSS) is a sampling scheme that selects a sample based on a baseline auxiliary variable for assessing survival time. Simulation studies show that this approach provides a more robust testing procedure, and a more efficient hazard ratio estimate than simple random sampling (SRS). The lung cancer survival data are used to demonstrate the method.
Risk Factors of Physical Condition of House and Clean and Healthy Living Behavior (PHBS) to Tuberculosis in Kaluku Bodoa Health Center Area, Makassar City
Background: Tuberculosis remains the 10th leading cause of death globally, accounting for approximately 1.3 million fatalities. The physical conditions of a house, including ventilation, humidity, temperature, occupancy density, lighting, and Clean and Healthy Living Behavior (PHBS), are crucial factors that should be considered in relation to TB incidence. Objective: This study aims to analyze the relationship between house physical conditions and PHBS with the incidence of TB in the working area of the Kaluku Bodoa Public Health Center, Makassar City. Methods: This study employed an observational analytic design with a cross-sectional approach. The sample size for the study comprised 150 respondents. Data were processed using univariate analysis, presented in tables, and further analyzed descriptively and bivariately using the Chi-square test to determine the relationship between house physical conditions and CHLB with TB incidence in the working area of the Kaluku Bodoa Public Health Center, Makassar City. Results: There was a significant relationship between ventilation, lighting, occupancy density, and PHBS with TB incidence in the Kaluku Bodoa Public Health Center, Makassar City. At the same time, temperature and humidity were found to have an insignificant effect on TB incidence. Conclusion: The findings of this study can be used to guide government policies aimed at improving the quality of life for individuals with TB. Environmental health officers can implement intensive programs emphasizing the importance of handwashing, maintaining cleanliness, and ensuring proper ventilation to reduce the risk of TB transmission.
Chronic kidney Disease Classification through Hybrid Feature Selection and Ensemble Deep Learning
Diagnosing and treating at-risk patients for chronic kidney disease (CKD) relies heavily on accurately classifying the disease. The use of deep learning models in healthcare research is receiving much interest due to recent developments in the field. CKD has many features; however, only some features contribute weightage for the classification task. Therefore, it is required to eliminate the irrelevant feature before applying the classification task. This paper proposed a hybrid feature selection method by combining the two feature selection techniques: the Boruta and the Recursive Feature Elimination (RFE) method. The features are ranked according to their importance for CKD classification using the Boruta algorithm and refined feature set using the RFE, which recursively eliminates the least important features. The hybrid feature selection method removes the feature with a low recursive score. Later, selected features are given input to the proposed ensemble deep learning method for classification. The experimental ensemble deep learning model with feature selection is compared to Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF) models with and without feature selection. When feature selection is used, the ensemble model improves accuracy by 2%. Experimental results found that these features, age, pus cell clumps, bacteria, and coronary artery disease, do not contribute much to accurate classification tasks. Accuracy, precision, and recall are used to evaluate the ensemble deep learning model.
The Effectiveness of the SOBUMIL mHealth App in Enhancing Early Detection of Pregnancy Complications in Bogor Regency, Indonesia
Background: Global and national efforts are underway to reduce maternal mortality. Empowering pregnant women enables health decision-making and early detection of pregnancy complications. Developing applications related to pregnancy potentially improves women's behavior in preventing pregnancy complications. Objective: This study aimed to explore the influence of SOBUMIL (Sobat Ibu Hamil), an android-based application on pregnant women's empowerment for early detection of complications. Methods: A quasi-experimental study was conducted in the Bogor Regency, Indonesia. Study participants were pregnant women residing in two primary health care in their second and third trimesters. Pregnant women were excluded if they were disabled or unable to read and write. A total sample of 350 was calculated using the Lemeshow sample formula, which included an intervention and control group. Results: Overall, we found a statistically significant positive effect of SOBUMIL application in all pregnant women's empowerment parameters to detect pregnancy complications early in Bogor Regency (p<0.001). Conclusion: This study confirms the positive influence of the SOBUMIL application in empowering pregnant women for early detection of pregnancy complications. This underscores the potential of mobile health interventions to enhance knowledge, attitudes, and abilities, enabling independent monitoring and addressing of pregnancy-related risks, ultimately improving maternal healthcare outcomes.
The Effect of Emotional Regulation for the Successful Treatment of Emotional Dependence in Young People
Regulation of one’s own emotional state is of great importance for a person’s mental health. The issue under research is related to determining emotional regulation approaches for the success of the treatment of emotional dependence in young people. Methods. It was possible to achieve the set goal based on the use of methods of analysis, observation, the Spann-Fischer Codependency Scale, and the Student’s coefficient. The emotional regulation approaches developed by the authors included social recovery, analysis of someone else’s problem and behaviour, problem solving, and art therapy. Results.It was found that the therapy had a positive effect on the respondents, enabling them to primarily develop the self-confidence skills (96%). Also, to develop a lack of need for constant approval (92%), and consideration of their own interests (93%). It was found that the level of the respondents’ emotional dependence decreased to a low level (84%) from the beginning of the study. The respondents noted that art therapy (53%) and socialization (47%), which became the basis of the treatment approaches, had almost the same positive effect. Conclusions.The practical significance of the article is related to the possibility of using effective approaches to regulating emotional dependence in young people. The research prospects will be aimed at comparing the impact of the developed approaches to regulating emotional dependence in young people and middle-aged people.
Policy Innovation in Healthcare: Exploring the Adoption and Implementation of Telemedicine
Background: Telemedicine has emerged as a transformative solution in healthcare, offering improved accessibility and efficiency. However, its widespread adoption remains influenced by policy frameworks, digital infrastructure, and financial sustainability. This study examines the role of policy innovation in telemedicine adoption and implementation, assessing regulatory impact, technological readiness, and reimbursement structures. Methods: A cross-sectional survey design with a mixed-methods approach was employed, integrating quantitative surveys and qualitative interviews. Data were collected from healthcare policymakers, administrators, physicians, and technology developers across hospitals, clinics, and telemedicine service providers. Logistic regression and chi-square tests were conducted to analyze key predictors of telemedicine adoption, including regulatory support, digital infrastructure, and reimbursement policies. A total of 400 participants were surveyed, and 25 stakeholders were interviewed to analyze key predictors of telemedicine adoption. Results: The findings indicate that institutions with clear licensing regulations and policy support exhibited significantly higher telemedicine adoption rates (OR = 2.15, p = 0.004). Standardized reimbursement policies positively influenced adoption rates (χ² = 14.91, p = 0.008). Digital infrastructure readiness, including broadband connectivity and EHR interoperability, was strongly associated with increased telemedicine utilization (OR = 2.31, p = 0.005). Major barriers included regulatory fragmentation, financial constraints, and technological literacy gaps. Conclusion: Policy innovation, digital infrastructure investments, and structured reimbursement models are critical for telemedicine expansion. Addressing regulatory inconsistencies and financial limitations will enhance adoption. Future research should explore long-term policy impacts and AI integration in telemedicine.
Boruta Feature Selection and Deep Learning for Alzheimer’s Disease Classification
Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and functional deterioration. The early and accurate classification of AD is crucial for timely intervention and management. This study utilizes the Boruta feature selection method to identify the most relevant features for AD classification, selecting the top 15 features based on importance ranking. Three machine learning models—Deep Neural Networks (DNN), Long Short-Term Memory Networks (LSTM), and Support Vector Machines (SVM)—were evaluated using accuracy, precision, recall, and F1-score as performance metrics. The LSTM model demonstrated the highest accuracy (89.30%), outperforming DNN (88.14%) and SVM (84.19%), owing to its capability of capturing temporal dependencies in inpatient data. Results indicate that deep learning models offer superior performance compared to traditional machine learning approaches in AD classification. The study emphasizes the importance of cognitive, lifestyle, and metabolic features in AD diagnosis while acknowledging limitations such as dataset constraints and model interpretability. Future research should improve explainability, incorporate multi-modal data, and leverage real-time monitoring techniques for enhanced AD detection.
A Choice of Performance Metrics for Evaluating Predictive Accuracy of Survival Models
This research critically assessed the predictive accuracy of parametric survival models (Weibull, Exponential, Log-logistic, and Gompertz) against penalized Cox PH models (Ridge, Lasso, and Elastic Net) using both simulated data (sample sizes of 100, 200, and 1000) and real-world data from the Nigerian Demographic and Health Survey (NDHS). The findings showed that parametric models, particularly the Weibull and Log-logistic models, consistently outperformed the others, achieving the highest Concordance Index (C-index) and the lowest Mean Absolute Error (MAE) and Mean Squared Error (MSE), indicating superior discrimination and calibration. In contrast, penalized Cox models underperformed, especially with a larger number of covariates, and the Gompertz model exhibited poor predictive performance under all conditions. Notably, parametric models remained stable and consistent even with smaller sample sizes and high-dimensional, complex data. These results highlighted the reliability of parametric models in survival analysis, particularly in small-sample and high-dimensional settings, offering key insights to inform future infant and child health research.
Adapting and Validating a Motor Intelligence Assessment Tool for Children with Intellectual Disabilities: Prioritizing Movement and Sensory-Motor Integration
Background: Motor intelligence, which involves the integration of sensory input and motor output, plays a crucial role in the physical, cognitive, and social development of children with intellectual disabilities (ID). While validated tools exist to measure motor intelligence in typically developing children, there is a significant gap in reliable and adaptable assessments for children with ID. Assessing motor intelligence in this population is essential for identifying sensory-motor deficits and designing targeted interventions to enhance physical performance, promote participation in physical activities, and improve overall quality of life. Objective: To evaluate the reliability, validity, and sensitivity of the adapted tool in identifying sensory-motor deficits and movement priorities specific to this population. The ultimate goal is to provide a practical and effective assessment tool that can inform targeted interventions to improve motor performance, physical activity participation, and overall developmental outcomes for children with ID. Methods: A total of 100 children aged 9–12 years with mild-to-moderate intellectual disabilities (IQ range 50–70) were randomly selected from a special education school in Assiut province, Egypt. The study adapted an existing motor intelligence test battery, originally designed for typically developing children, to better suit the sensory-motor and cognitive abilities of children with ID. The adapted battery included tasks evaluating sensory-motor coordination, balance, motor planning, and movement prioritization. Modifications were made to simplify instructions, reduce task complexity, and incorporate visual and auditory cues to accommodate the unique needs of children with ID. Reliability and validity were assessed using Pearson’s correlation coefficients and t-tests, while factor analysis was conducted to identify key dimensions of motor intelligence in this population. Results: The motor intelligence test battery demonstrated high reliability (r = 0.813 to 0.999) and validity (t-values ranging from 7.98 to 9.33; p < 0.01). Tasks such as "Consecutive Jumps" (r = 0.980) and "Sound and Motion" (r = 0.915) showed excellent reliability, indicating their suitability for children with ID. However, tasks requiring more complex coordination, such as "Rolling Ball," exhibited moderate reliability (r = 0.529), suggesting the need for further refinement or alternative task designs for this population. Factor analysis revealed five distinct dimensions of motor intelligence, collectively explaining 35.65% of the variance, which aligned with the movement priorities and sensory-motor challenges specific to children with ID. Standardized score tables were developed to ensure fair and accurate interpretation of test results, accounting for the variability in motor abilities within this population. Conclusion: The adapted motor intelligence test battery proved to be a reliable and valid tool for assessing motor intelligence in children with intellectual disabilities. The modifications made to the original test battery ensured its appropriateness for this population, enabling the identification of sensory-motor deficits and movement priorities. The study highlights the importance of tailoring assessment tools to the unique needs of children with ID, ensuring accurate measurement and meaningful interpretation of results. The researcher recommends the inclusion of the adapted motor intelligence battery and the standardized score tables in related programs within intellectual schools to support the development of targeted interventions. These interventions can enhance motor performance, promote physical activity participation, and improve overall quality of life for children with ID.
motor performance for children with special needs
Scoring System Model for Early Detection of Maternity Blues in Bukittinggi, West Sumatera, Indonesia
Background: Maternity blues creates emotional instability in moms, causing them to become irritated, overly nervous, and feel incapable of being a good mother. Maternity blues may interfere with infant care and raise the risk of postpartum depression symptoms, disrupting mother and baby interactions. Maternity blues is often ignored so it is not diagnosed and if not treated properly it can become a problem and develop into postpartum depression or postpartum psychosis. Maternity blues is a serious condition that poses risks to both mothers and infants. If left untreated, Maternity blues can progress into postpartum depression, which has significant physical and psychological consequences. Early detection of Maternity blues is crucial for timely intervention and prevention. Objectives: This study aims to develop a Scoring System Model for the early detection of maternity blues , allowing for effective screening and timely management. Methods: A cross-sectional study was conducted in Bukittinggi City, West Sumatra, Indonesia, involving 126 postpartum mothers recruited consecutively. Data analysis included the calculation of odds ratios, logistic regression, and ROC curve analysis to determine the sensitivity and specificity of the prediction model. The scoring system's performance was assessed using calibration and discrimination values. Results: The developed scoring system demonstrated good calibration and discrimination, with an Area Under the Curve (AUC) value of 0.806 (95% CI: 0.732–0.881). The Hosmer & Leme show test showed a p-value of 0.724, indicating a good fit for the model. Conclusion: The proposed scoring system is a reliable tool for the early detection of maternity blues . By identifying at-risk mothers through prediction scores, appropriate interventions can be implemented to prevent the progression of maternity blues into more severe postpartum mental health disorders.
Analysis of Occupational Health and Safety Risk Management: Hazard Identification, Risk Assessment, and Risk Control-HIRARC for Workers at Health Quarantine Offices in Makassar, Indonesia
Work hazards and risks are closely related to occupational activities and have the potential to cause injuries and occupational diseases. Every workplace carries the risk of accidents, as reflected in data from Indonesia's Work Accident Insurance Program (JKK BPJS Ketenagakerjaan). The number of workers experiencing fatalities due to occupational accidents and diseases decreased from 4,007 cases in 2019 to 3,410 cases in 2020 but increased again to 6,552 cases in 2021. This study aims to assess occupational health and safety risk management using the Hazard Identification, Risk Assessment, and Risk Control (HIRARC) method among workers at the Makassar Health Quarantine Center. This descriptive study involved a population of 133 workers, with a sample of 57 workers selected using simple random sampling. Data were collected using the HIRARC questionnaire and analyzed using univariate analysis. The results showed that the majority of respondents were aged 40–49 years (57.9%), and 73.7% worked more than 8 hours per day when assigned to night shifts. The HIRARC assessment identified that the most common occupational hazard experienced by workers was ergonomic risk, with complaints of back, waist, and shoulder pain, classified as a moderate risk. In conclusion, ergonomic hazards pose a significant issue among workers, categorized as a moderate risk level. Therefore, it is recommended that the Makassar Health Quarantine Center enhance its occupational health and safety risk management and conduct regular evaluations of workplace hazards and risks.
Innovative SiKaRen Smartphone Application Model: A Breakthrough in Enhancing IMP Cadres’ Knowledge and Attitudes Toward Family Planning
Objectives: There are several challenges in the implementation of family planning programs in urban areas, one of which is to enhance the capacity of the urban community institutions (IMPs) as the first level of family counselling and support. The other major constraint is the limited availability of new and efficient training techniques, because conventional training is not very efficient in enhancing the knowledge and beliefs of IMP cadres. In the digital era, the use of smartphone-based technology could be the solution to improve the effectiveness of cadre training in a more flexible and interactive way. This study was conducted to assess the efficacy of the smartphone based SiKaRen application in enhancing the knowledge and attitudes of IMP cadres. Methods: This study used a mixed-methods design, combining quantitative and qualitative approaches. The quantitative aspect employed a quasi-experimental design with pre- and post-tests on two groups: an intervention group using the SiKaRen Applications and a control group with conventional training. The qualitative approach explored the roles, knowledge, and attitudes of 10 Informants through in-depth interviews, focus group discussions (FGDs), and standardized questionnaires. Data were analyzed using statistical tests. Results: The problem of the cadres was that they did not have the required knowledge about the advantages, disadvantages and side effects of various contraceptive methods, hence lacked the confidence of providing advice. When they encounter challenges, they just quit, but they do attempt to help and look for assistance. Furthermore, the role of the cadres is not optimal due to limited facilities, not clearly defined functions, missing documents and lack of innovation. The SiKaRen model based on a smartphone was found to have a significant effect in enhancing the knowledge and attitude of the cadres in the field (p-value < 0.05) and therefore could be a way of solving the problems faced by cadres in the field. Conclusions: The integration of technology into the SiKaRen model enhances the ability of cadres to receive the latest information and to track and monitor family planning participants more effectively. This digital application also enables more precise interventions based on accurate data, meaning that cadres are not only facilitators, but also drivers of family planning awareness.
Spatial Analysis Risk Factors of Pneumonia Incidence in Toddlers Gowa Regency
Background: Pneumonia is one of the highest causes of death in children under five years old in the world. Globally, the number of under-five deaths due to pneumonia is estimated to reach up to 700,000 cases per year. Objectives: This study aimed to spatially analyze the risk factors for pneumonia incidence among under-fives in Gowa Regency in 2021-2023. Methods: This study used an analytic observational with an ecological study design. The population in this study was all cases of pneumonia among under-fives in Gowa Regency in 2021-2023, totaling 1,634 cases. The sample size in this study was 18 subdistricts with the sample selection technique using the exhaustive sampling method. Results: There was a relationship between population density (r=0.470 p=0.000), poor population (r=0.422 p=0.001) and incomplete immunization status (r=0.457 p=0.000) with the incidence of pneumonia among under-fives in Gowa Regency in 2021-2023. Meanwhile, there was no association between undernutrition status (r=0.250 p=0.068) with the incidence of pneumonia among under-fives in Gowa Regency in 2021-2023. Conclusion: Although undernutrition status did not show a statistically significant association in this study, it remains an important risk factor in the susceptibility of under-fives to pneumonia and other infections. Children with undernutrition status have a weak immune system, making them susceptible to disease complications. Therefore, nutritional interventions such as the provision of supplementary food, increasing exclusive breastfeeding coverage, and nutrition education to parents still need to be developed in a sustainable manner.
Raking Method as a Tool for Improving Representativeness in Non-Probability Studies
This is a methodological review focused on raking, or iterative proportional fitting, as a tool for improving representativeness in studies with non-probability sampling. The paper synthesizes the theoretical foundations, practical considerations, and applications of raking in biomedical research. The method operates by iteratively adjusting sample weights so that the marginal distributions of selected variables match the known distributions of the target population. Its implementation requires reliable auxiliary information about the population of interest and careful selection of adjustment variables. The review addresses critical aspects such as weight quality evaluation, management of extreme values, and computational considerations in raking implementation. The method's advantages are discussed, including its capacity to simultaneously adjust multiple variables and its applicability when only marginal information about the population is available. Its limitations are also examined, such as the potential generation of extreme weights and dependence on precise population data. Finally, practical examples are presented in various contexts, from hospital studies to research in university populations, demonstrating the method's versatility. The application of raking has proven particularly valuable in epidemiological and health services studies, where non-probability samples are common. This review provides a comprehensive methodological guide for researchers seeking to implement raking, emphasizing the importance of rigorous application and transparent documentation.
Strengthening the Health System to Address the COVID-19 Surge: An Empirical Study in South Kalimantan Province, Indonesia
The COVID-19 case entered South Kalimantan Province on May 12, 2020, and spread throughout all districts/cities. This research aims to examine the ability of the South Kalimantan Provincial Health System to address the COVID-19 pandemic. This study analyses secondary data from the South Kalimantan Provincial Health Office and in-depth interviews with policymakers. This study assesses the capacity of the South Kalimantan health system in managing the COVID-19 pandemic. Findings reveal significant challenges, including hospital bed shortages, high infection rates among health workers (10.02%), and limited ventilator availability. Despite allocating 23.27% of the health budget to the pandemic response, key subsystems such as human resources, drug supply, and coordination mechanisms remained under strain. Strengthening these subsystems is essential for better preparedness in future health emergencies. In conclusion, strengthening the health system is very important in overcoming the COVID-19 pandemic, and it is hoped that the lessons of the COVID-19 pandemic will make the health system more prepared to address the disease pandemic.
Management of Antipsychotic Therapy in Patients with Schizophrenia
Antipsychotic therapy is the main approach in the treatment of schizophrenia, but there is often irrational use due to inappropriate drug selection, inappropriate dosage, and long-term use without evaluation. Factors that support therapeutic rationality include adherence to clinical guidelines, selection of safer antipsychotics, and optimal management of side effects. Therefore, it is important to evaluate the factors that contribute to rational and irrational therapy in the use of antipsychotics in patients with schizophrenia. This study aims at antipsychotic medication management and factors that cause irrational therapy, as well as evaluating factors that support therapeutic rationality in the use of antipsychotics in schizophrenic patients. This study used a cross-sectional study design involving schizophrenia patients undergoing antipsychotic therapy in a psychiatric hospital. Data were collected through patient medical records and interviews with health workers. Quantitative data were analyzed using descriptive statistics and inferential tests, including chi-square and regression analysis, to determine the association between patient characteristics and antipsychotic selection as well as therapy rationality. The results showed that 26.7% of patients received irrational therapy, with the main causes being inappropriate drug selection (45%), inappropriate dosage (30%), and long-term use without evaluation (25%). Meanwhile, 73.3% of patients received rational therapy, with the main contributing factors being adherence to clinical guidelines (50%), selection of safer antipsychotics (30%), and good side effect management (20%). Irrational antipsychotic therapy remains a significant problem in the management of schizophrenia. Adherence to clinical guidelines and appropriate therapy selection can improve treatment effectiveness and reduce the risk of side effects. Regular evaluation and a multidisciplinary approach are needed to improve the rationality of antipsychotic therapy.
The Influence of Emotional Intelligence on Coping Skills
Background: The relevance of the study is determined by the interest in studying the influence of emotional intelligence (EI) on stress resistance, which is of great importance in view of numerous stress factors. Objective: The aim of the study is to determine the influence of EI on coping skills and the choice of coping strategies. Methods: The study employs a test method (Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT), Holmes-Rahe Stress Inventory). The Coping Strategies Questionnaire (CSQ) was also used. The results were processed using statistical methods (mean, range, mode and median, the Mann-Whitney U test, Pearson correlation coefficient (PCC)). The factor analysis was carried out. Results: More pronounced emotion regulation (weight 0.53) have been found in men, while women better recognize emotions (weight 0.45). The correlation between the level of EI and adaptive strategies is confirmed: high EI reduces the negative cumulative effect of stress (M = 55 in a group with high EI). High EI is related to active stress strategies, such as planning and seeking social support, confirming its role as a protective factor. Conclusion: It can be argued that the high EI significantly reduces the frequency, intensity of stress and its impact, facilitating adaptive strategies for overcoming it. Further studies may focus on the influence of EI on stress resistance in different age and cultural groups, as well as on long-term effects in the context of professional stress.
The Impact COVID-19 Pandemic on Coronary Heart Disease Deaths: Using Bayesian Lorenz Curve and Gini-Index Distribution
Aim: The aim to investigate and assess role of COVID-19 on Coronary Heart Disease (CHD) mortality using Bayesian Lorenz Curve and associated Gini Index Statistical Method: Bayesian estimation was applied to analyze CHD mortality rates, focusing on both gender and age group differences. Application: A total of 341,467 patients were treated during 2-year period from 2020 to 2021during COVID-19 in Turkey. 195,413 females and 146,054 males were diagnosed and 155,211 deaths where 88,824 were males and 66,387 were females with CHD, and hence were studied to evaluate whether female gender was an independent predictor for poor prognosis. Results: Mortality rates increase with age for females compared to males. The model suggests that males have higher risks or proportions across all groups compared to females, particularly in older age categories. The Lorenz curves for both genders show that a significant portion of deaths is concentrated in a relatively small subset of age groups, particularly older adults. The Gini Index regarding mortality for males is found to be 0.123 compared to value of 0.384 associated with female's age distribution. Meanwhile, the Gini Index regarding morbidity for males (0.146) and females (0.394) are very similar, suggesting that the patterns of inequality in morbidity distribution are comparable across genders. Conclusion: The study highlights the effectiveness of empirical Bayesian techniques in estimating CHD-related COVID-19 mortality rates across Turkey. It suggests that such statistical methods can help allocate resources more efficiently to high-risk areas and to ensure fair resource distribution and healthcare interventions.
RPCA with Log-Schatten Norm and Adaptive Histogram Equalization for Medical Imaging
Medical imaging, especially cancer and retinal fundus analysis, is often compromised by artifacts and heavy noise and artifact, which can hinder accurate diagnosis. Existing low-rank sparse component methods, such as RPCA with the conventional nuclear norm, assume uniform singular value weights, which may not hold true due to noise variations in images. We recently developed RPCA with the log-weighted nuclear norm, which addresses some of these issues but still relies on weight selection, potentially introducing bias. To overcome these limitations, we propose a novel method that integrates RPCA with Log-Schatten Norm (LSN) and Adaptive Histogram Equalization (AHE) for medical imaging and clinical purposes. The Log-Schatten Norm improves singular value penalization and structure preservation, while AHE enhances contrast and reduces noise. The method is formulated as an optimization problem and solved using the Alternating Direction Method for Multipliers (ADMM). Experimental results on publicly available retinal and cancer image datasets demonstrate that our method outperforms existing methods in enhancing overall image quality, making it a promising tool for medical imaging applications.
Response Adaptive Randomization Using Biomarkers with Exponentially Decreasing Probability Sequence
In this article, it is proposed to study the application of Response Adaptive Randomization (RAR) design in clinical trials. The approach involves the prediction of treatment outcomes based on the biomarker of patients using a regression model. The focus is on rare diseases to efficiently allot the patients among various treatments so as to ensure not only the clinical rights but also the maximum possible benefits to the patients even when they are in clinical trials. Initially, the method uses conventional equal randomization to understand how well every treatment works in patients and this initial duration is known as burn-in period. The proposed work allocates patients to treatments by using an exponentially decreasing probability sequence instead of the existing linearly decreasing sequence to have higher allocation probability to the efficient treatment. In the case of rare disease, it is observed from simulation study that the use of exponentially decreasing probability sequence in RAR design increases the benefit to the patients in the clinical trials when compared to the existing method that uses linearly decreasing sequence. The study also investigates the performance of the proposed RAR design when used with different regression methods under various scenarios. The performance of the proposed design is measured by the proportion of patients assigned to the best treatment in addition to Type I error and power. From the impressive results, it is suggested that the proposed RAR design can be implemented practically in clinical trials of rare diseases without any apprehension.
Exponentially decreasing probability sequence
A Hybrid Time Series–Regression Model for Tuberculosis Forecasting in Resource-Limited Settings
Tuberculosis (TB) is still a serious public health issue in Sudan, especially in Gedaref State, because of limited medical facilities and inadequate disease reporting. This experiment develops a forecasting model by employing Seasonal Trend decomposition using LOESS (STL) and linear regression in combination, relying on the weekly tests to improve TB prediction. The model improves the accuracy of its forecasts by combining time series information with the details of the daily operations of the health system. Weekly data from Gedaref showed that the STL + regression approach performed better than ARIMA, reducing the root mean squared error (RMSE) from 2986.85 to 540.95, an improvement of about 81.9%. The model also remained flexible to fluctuations in testing volume. The findings illustrated that hybrid statistical methods have been proved to be reliable and practical in forecasting TB cases in situations where limited resources exist, providing a strong base for overseeing TB and other communicable diseases.
Comparison of Heterogeneity Measures in Meta-Analysis
Background: Heterogeneity assessment is critical in meta-analysis, as it determines the appropriateness of combining studies and affects result reliability. Cochran’s Q is the traditional test, nevertheless, it has low statistical power, so many researchers resort to using heterogeneity measures to quantify the heterogeneity. Aim: This article aims to compare the performance of the most commonly used heterogeneity measures through simulation. Materials and Methods: We compared the performance of four heterogeneity measures (, , , H) across various homogeneous and heterogeneous patient-event probabilities [], various sample sizes (n) and number of studies (k), using RMSE (Root mean squared error) and BIAS values in simulation scenarios. Additionally, Cochran’s Q Type-I error rate and power were evaluated using the same simulation scenarios. Results: and H outperformed other measures in large samples, while , and were preferable for small studies. Conclusion: Researchers can use the simulation results from this study to select an appropriate heterogeneity measure for their meta-analysis work. This approach is expected to prevent time loss due to unnecessary subgroup analyses in situations where heterogeneity appears to be present but is actually absent.
Overestimation of Cardiovascular Mortality Risk by Kaplan-Meier in Competing Risks Settings: A Web-Based Calculator and NHANES Analysis
Background: Traditional Kaplan-Meier (KM) event rates are widely used for cardiovascular risk prediction and tend to overestimate absolute event risk for patients by censoring competing events, such as non-cardiovascular death. Competing risks analysis (CRA), which account for such terminal events, offers more accurate estimates. However, its application in a web-based health analytics remains limited. Methods: Using a simulated cohort (n = 2,500; 100 repetitions) and the 1999–2000 NHANES cohort (n = 2,480) with 2019 National Death Index mortality linkage, the researcher compared KM estimates to CRA’s Cumulative Incidence Function (CIF), implemented via Aalen-Johansen estimators and Fine-Gray subdistribution hazard models. We assessed relative differences (bias) in 5-, 10-, 15-, and 20-year cardiovascular mortality predictions across risk strata. Findings informed a web-based calculator prototype that dynamically estimates age-specific KM and CIF probabilities while highlighting potential misclassification risks. Results: KM consistently overestimated cardiovascular mortality risk compared to CIF. In the NHANES cohort, KM estimated the 5-year risk to be 5.85% higher than the actual rate (4.37% vs. 4.13%) and 20-year risk by 28.3% (20.02% vs. 15.60%). In the simulated data, KM overestimated the 5-year risk by 7.63% (5.84% vs. 5.42%) and the 20-year risk by 31.17% (21.37% vs. 16.25%). KM-based models tend to misclassify a substantial portion of patients into higher-risk groups compared to CIF-adjusted models. Conclusion: This study demonstrates that Kaplan-Meier consistently overestimates cardiovascular mortality in comparison to competing risk methods across five time points, through using both simulated and nationally representative data. We quantify this overestimation and provide an online calculator that shows differences by age. Our tool improves the usability and interpretability of competing risks analysis for older adults in digital health settings, in contrast to tools like SCORE2.
Statistical Analysis of Logistics Management Impact on Medical Device Indicators in Indonesian Island Clinics
Background: Public health centers have a strategic role in the implementation of primary health services, to meet minimum service standards. The ASPAK application is used as an instrument for monitoring facilities, infrastructure, and medical devices, but the achievement of medical device indicators in Banggai Laut district is still very low, at 27.87%, which indicates obstacles in the logistics management of medical devices. This study aims to analyze the relationship between logistics management and medical device indicators on the ASPAK Application. Methods: This study employed a quantitative approach with a cross-sectional design. Statistical analysis was conducted using the Pearson Product-Moment Correlation test and multivariate regression to examine the relationship between logistics management and medical device indicators on the ASPAK application across 10 health centers in Banggai Laut District. The research was carried out from September to December 2024, involving a total population of 70 individuals, all of whom were included as the study sample. Result: There is a relationship between logistics management sub-variables and medical device indicators (r=0.583-0.659; p=0.000). Multivariate analysis, planning, and deletion are significantly related (y = 6.877 + 0.437 planning + 0.481 deletion), with an R-square value of 0.638, which means 63.8% of the variation in indicator achievement is explained by the model. Conclusion: Planning and deletion have a positive correlation and are the largest contributors to the fulfillment of the ASPAK medical device indicator. This finding emphasizes the importance of both aspects in supporting indicator achievement.
Association between Body Mass Index and Complete Blood Count Parameters
Obesity is an abnormal or extraordinary cumulative fat in the body which can lead to health problems. Its prevalence increased worldwide. This study aims to identify the effect of obesity on CBC parameters in obese subjects. Two hundred obese patients of either gender, the mean age was (33.71±2) years, 148 were females (74.4%) and 52 were males (25.6%), each subject submitted to medical history, physical examination, and CBC test. The results of the study revealed that there was a correlation between BMI and CBC parameters, (P value <0.05). This study concluded that there is positive correlation between CBC and BMI (positively correlated, p-value <0.05).
Structural Equation Modeling of Oral Stomatitis and Its Determinants among the Sundanese Ethnic Group: Evidence from IFLS-5
Background: Oral stomatitis is an inflammation of the mucosa in various oral structures such as cheeks, gums, tongue, lips, palate, and floor of the mouth that commonly occurs in communities, including among the Sundanese ethnic group. Risk factors affecting stomatitis incidence in the Sundanese population need to be analyzed for developing more effective prevention programs. Aim: To analyze risk factors for stomatitis among the Sundanese population using panel data from the Indonesian Family Life Survey (IFLS). Method: This was an analytical observational study using secondary data from IFLS-5. The research design employed structural equation modelling (SEM) analysis examining variables including age, gender, education, residential area classification, general health status, and smoking habits. Results: The study revealed that age and general health variables had significant associations with stomatitis occurrence (p<0,001). Ages below 25 years and suboptimal health conditions proved to be significant factors influencing increased stomatitis incidence. Meanwhile, gender, education level, residential area classification, and smoking habits showed no significant correlation. Conclusion: Age and general health status are the main risk factors for stomatitis occurrence among the Sundanese population, which can serve as a reference for prevention program development.
A Retrospective Study on Postoperative Complications in Gynecological Surgeries: Identification of High-Risk Factors and Best Practices
Background: Laparoscopic gynecologic surgery is widely favored for its minimally invasive nature, offering reduced postoperative pain, shorter hospital stays, and faster recovery. However, despite its advantages, postoperative complications—ranging from minor infections to major injuries—remain a concern. Identifying patient- and procedure-specific risk factors is critical to enhancing surgical safety and outcomes. Objective: To evaluate the incidence and predictors of postoperative complications in gynecologic laparoscopic surgeries and identify high-risk patient and procedural factors using a large, retrospective dataset. Methods: This retrospective cohort study included 15,308 patients who underwent laparoscopic gynecologic procedures at tertiary care hospitals in Pune, India, between January 2023 and October 2024. Patients were categorized by procedure type: adnexal surgery, myomectomy/uterine lesion surgery, LAVH/TLH, and malignancy surgery. Data on demographics, prior surgical history, comorbidities, and surgical details were collected. Complications were classified as major (e.g., bowel or ureteral injury, hemorrhage requiring reoperation) or minor (e.g., infection, transient fever). Multivariate logistic regression identified independent risk factors for major complications. Results: The overall major complication rate was 0.51%, and the minor complication rate was 4.64%. Surgeries for malignancy had the highest major complication rate. Independent risk factors for major complications included age 31–60 years (aOR: 2.88; 95% CI: 1.89–7.88), age >60 years (aOR: 2.92; 95% CI: 1.67–5.65), prior abdominal surgery (aOR: 3.58; 95% CI: 1.38–6.54), obesity (aOR: 2.52; 95% CI: 1.39–7.28), and higher surgical complexity (e.g., malignancy surgery vs. adnexal: aOR: 7.62; 95% CI: 3.61–13.63). Conclusion: Although complication rates in laparoscopic gynecologic surgery remain low, advanced age, obesity, previous abdominal surgery, and complex procedures significantly increase the risk of major complications. These findings underscore the need for thorough preoperative assessment, individualized surgical planning, and targeted risk mitigation strategies to optimize patient outcomes.
A Refined Population Mean Estimator Using Median and Skewness: Applications to Breast Cancer and Brain Tumor Data
Estimators are essential to sampling theory because they allow researchers and statisticians to calculate estimates of population parameters from observed data. In every survey activity, the experimenter aims to use methods that will improve the precision of population parameter estimations throughout both the design and estimation phases. When auxiliary data is used in the estimating, design, or both processes, these estimated precisions can be attained. By linearly merging the central value of the data under consideration with the skewness coefficient provided by Karl Pearson, this study created a new, improved predictor for calculating the average of a population. Estimators are crucial to sampling theory because of their capacity to produce estimates of population parameters from observed data. In this work, a novel modified ratio-type estimator was constructed by linearly merging Karl-Pearson's coefficient of skewness with the median value. Simple random sampling (SRS) was the technique employed in this present study. We conduct a numerical analysis from the standpoint of real estate. Additionally, we do some real data analysis on two distinct cancers: the brain tumor dataset and the breast cancer dataset. The results of the simulation study, real data application in the medical field, and numerical investigation show that the suggested estimator achieves lower error when the median value and Karl Pearson's coefficient of skewness are combined. Furthermore, compared to the other estimators under consideration, the one proposed in this study achieves better precision.
The Role of Emotion Regulation in the Vicarious Trauma Risk Reduction among Psychotherapists
The relevance of the study is determined by the increasing risk of vicarious trauma among psychotherapists because of their high emotional and professional stress. The study explores the link between emotion regulation training and vicarious trauma risk, well-being, and professional functioning in psychotherapists. The study employed the methods of testing and questionnaire survey (Emotion Regulation Questionnaire (ERQ), Perceived Stress Scale (PSS), and Professional Quality of Life Scale (ProQOL)). The following statistical methods were also used: descriptive statistics, Shapiro-Wilk test, paired t-test, Wilcoxon test, effect coefficients, multiple regression analysis, 95% confidence intervals. The reliability of the instruments was tested using Cronbach α. The intervention was associated with improvements in emotion regulation, stress reduction, and vicarious trauma symptoms (all p < 0.001). However, the absence of a control group precludes definitive causal attributions, as changes may reflect external factors (e.g., natural recovery, concurrent supervision). Despite this limitation, effect sizes (Cohen’s d = 0.55–0.70) and 12-month stability suggest clinical promise warranting future RCTs. Changes remained stable over the year, but some indicators showed partial regression. The women demonstrated higher levels of well-being, while gender differences on other parameters were minimal (p = 0.041). Furthermore, findings are contextually bound to urban psychotherapists in Kyiv due to purposive sampling; generalizability to rural settings or distinct healthcare systems requires verification.
Childhood Tuberculosis: Epidemiology, Etiology, Pathophysiology, Pathogenesis, Risk Factors, Prevention and Diagnosis: A Narrative Review
Background: Tuberculosis is a chronic infectious disease caused by Mycobacterium tuberculosis bacteria. These bacteria are rod-shaped and acid-resistant, so they are often known as acid-resistant bacilli (BTA). Children are at high risk of TB infection, especially infants and toddlers. Children infected with TB are at risk of developing severe TB disease that can lead to death or long-term disability. This article review focuses on a brief introduction to anemia, its etiology, pathophysiology, impact and preventive measures. Methods: Major databases including Scopus, Pubmed, Proquest, Google Scholar, and Science Direct were searched for articles related to pediatric tuberculosis. The keyword used in the literature search was “pediatric tuberculosis”. The time frame of the articles obtained ranged from 2016 to 2023. Results: This study shows that the burden of pediatric tuberculosis (TB) remains high globally and nationally. WHO reported 1.2 million cases of pediatric TB with more than 200,000 deaths, mainly in children under five. In Indonesia, pediatric TB cases increased from 110,881 (2022) to 129,798 (2023), with bacteriological confirmation still low, especially in children <5 years. In South Sulawesi, the trend of cases is decreasing, but in Parepare City it has increased from 16 to 21 cases. The main obstacles include difficulty in diagnosis, limited facilities, low coverage of TB preventive therapy (TPT), and suboptimal handling of drug-resistant TB. Conclusion: Diagnosing TB in children remains difficult due to limited tools and low bacterial presence. Many cases go underdiagnosed or overdiagnosed, while drug-resistant TB poses added risks. Prevention includes BCG vaccination, controlling risk factors, and TPT. Disparities in diagnosis and treatment persist. Early detection, family education, and access to care are vital. Strengthening community and household approaches can narrow diagnostic gaps and prevent complications in childhood TB.
Improving Alzheimer’s Disease Detection with Transfer Learning
Accurate and prompt diagnosis of Alzheimer's disease (AD) remains a challenge, with only a small percentage of patients receiving timely confirmation. Manual interpretation of MRI scans, the primary diagnostic tool, is time-consuming, subjective, and prone to error, particularly in differentiating between disease stages. This study aimed to develop a computer-aided diagnosis system (CAD) for AD classification using deep learning models. MobileNetV1 and Xception architectures were employed to classify AD into four stages: mild, normal, moderate, and severe. Transfer learning and layer freezing techniques were applied for feature extraction and classification. Model performance was evaluated using precision, recall score, and accuracy metrics. The Xception model achieved a higher accuracy (79%) compared to MobileNetV1 (73%) in classifying AD stages. Compared to MobileNetV1, this study shows that Xception-based CAD systems have the potential to diagnose AD more accurately, providing a promising path for future research and clinical application.
Statistical Evaluation of Comorbidities and Environmental Factors in COVID-19 Outcomes: Risk Measures and Predictive Analysis Using Odds and Hazard Ratios
In this study, we have reviewed studies that highlight the effects of common conditions like heart disease, high blood pressure, congestive heart failure, kidney problems, and emphysema, which offer the largest risk of death caused by COVID-19. Also, explored how physical activity can influence one's resistance to COVID-19, the effects of stat in medications on mortality rates, and the effectiveness of vaccines in reducing fatalities offer valuable avenues for tailored interventions and treatment strategies and the examination of the relationship between exposure to air contamination and the severity of COVID-19 highlights the task of environmental factors in shaping the outcomes of the illness. Primarily, we focused on how comorbidities affect COVID-19 patients and the associations between comorbidities, lifestyle factors, environmental influences, and COVID-19 outcomes, guiding healthcare strategies and future research, and refining responses to the ongoing pandemic. In this study, analysis of COVID-19 studies centered on compiling risk assessments, including 95% confidence intervals along with odds and hazard ratios. The analysis's goal was to gather and assess the different risk metrics provided in this study.
The Effect of Website “Remaja Cegah DBD” on the Prevention Behaviour of Dengue Haemorrhagic Fever Among Students at Junior High School in Makassar City, Indonesia
Background: Dengue Haemorrhagic Fever (DHF) is an acute infectious disease caused by the dengue virus. It occurs frequently children and adolescents. In 2023, the incidence of DHF in Makassar City was 26 per 100,000 population. Preventive efforts, such as increasing community awareness through health education at the school level, need to be initiated. Objectives: The aim of this study was analyze the effect of the "Remaja Cegah DBD" (Youth Prevents DHF) website on dengue prevention behavior among students at Public Junior High School (SMPN) in Makassar. Methods: This study was a quasi-experimental design with a non-randomized pre-test and post-test control group design involving 122 students, the main intervention group consisted of 61 respondents at SMPN 8, and the comparison intervention group included 61 respondents at SMPN 6. Data were collected from August 22 to September 6, 2024. Data analysis was conducted using univariate and bivariate methods, including Paired t-test, Wilcoxon test, Independent t-test, and Mann-Whitney test, with Stata Program version 14. Results: There was a significant difference in DHF prevention knowledge before and after the website intervention (p=0.000). There was also a significant difference in attitudes (p=0.000) and practices (p=0.000) before and after the website intervention. Furthermore, there were significant differences in prevention behavior between the main intervention group (website) and the comparison group (leaflet), with statistical test results for knowledge (p=0.000), attitude (p=0.000), and practice (p=0.0016). Conclusion: Website was more effective as an educational prevention of DHF among adolescents or students in Makassar City, compared to leaflet-based media.
Safety and Efficacy of Flow Diversion for Intracranial Aneurysms in Small Parent Vessels: A Retrospective Cohort Study
Background: Intracranial aneurysms arising from small parent vessels (<2 mm) are challenging due to their fragile anatomy and distal locations. Flow diversion (FD) offers a promising treatment, but its safety and efficacy in small vessels remain underexplored. Objective: To evaluate angiographic occlusion rates and procedure-related morbidity associated with flow diversion for intracranial aneurysms arising from parent vessels <2 mm in diameter. Methods: A retrospective cohort study was conducted at Bicêtre Hospital, Paris, France, from January 2018 to December 2023, analyzing 50 patients undergoing 56 procedures for 55 aneurysms. FD devices (e.g., Silk Vista Baby, Pipeline Flex) were used. Follow-up at 6, 18, and 42 months assessed occlusion via digital subtraction angiography (DSA) using Raymond-Roy and O’Kelly-Marotta scales. Results: Complete occlusion was achieved in 70.9% (39/55) of aneurysms at a mean follow-up of 17.86 months. At 6, 18, and 42 months, occlusion rates were 56.4%, 55.6%, and 76.5%, respectively. Intraprocedural complications occurred in 28.6% (16/56) of procedures, with 19.6% (11/56) due to in-stent thrombosis. Symptomatic major complications were observed in 17.9% (10/56), including ischemic events and hemorrhage, and permanent FD-related morbidity was 8.0% (4/50). Conclusion: FD is effective for small vessel aneurysms, with high occlusion rates, but significant complications highlight the need for careful patient selection and refined techniques. Larger studies are needed to optimize outcomes.
Exploring the Relationship between Perceived Multidimensional Social Support and Well-Being among Community Seniors Participating in Group Exercise Programs
This study examines the relationship between perceived multidimensional social support and well-being among community-dwelling seniors engaged in group exercise programs, aiming to address a gap in the current literature. A total of 162 older adults from two communities in Selangor, Malaysia, participated in structured group exercise sessions. Using a quantitative survey design, data were collected through the Multidimensional Scale of Perceived Social Support (MSPSS) and the BBC Well-Being Scale to assess perceived support and overall well-being, respectively. Community leaders were engaged to support the study’s approval and facilitate its implementation. Descriptive statistics were used to summarize participants’ demographic characteristics and study variables, while Pearson correlation analysis was conducted to examine the relationships between perceived social support and well-being. The sample consisted predominantly of males (63%), reflecting a gender imbalance in exercise participation that aligns with findings from previous studies. Results showed no significant correlation between perceived social support and overall well-being (r(160) = -0.113, p > .05), indicating a weak negative relationship. This suggests that higher levels of perceived social support did not correspond with notable improvements in well-being among the participants. These findings challenge the prevailing literature that often highlights a positive association between social support and well-being, suggesting that the relationship may be more nuanced within the context of community-based group exercise programs for older adults. Further investigation is needed to uncover the underlying factors that may influence this relationship in this specific demographic.
Multidimensional Social Perceived Social Support
Guillain-Barré Syndrome in Egypt: Diagnostic Challenges and Subtype Evolution Over Time
Background: Guillain-Barré Syndrome (GBS) is the leading cause of acute flaccid paralysis worldwide, with diverse clinical and electrophysiological presentations. Regional variation in GBS subtypes exists, but limited data are available from Arab countries, particularly Egypt. Objective: This study aimed to characterize clinical features, neurophysiological patterns, and disability outcomes among Egyptian GBS patients and evaluate changes in electrophysiological subtypes over time. Methods: A prospective observational study was conducted on 49 adult patients diagnosed with GBS and admitted to Mansoura University Hospitals between May 2022 and May 2025. Clinical data, disability scores (Hughes, INCAT, mEGOS, MRC), and electrophysiological findings were collected within the first two weeks of symptom onset and repeated before discharge. Electrophysiological subtyping was performed using Rajabally’s criteria at initial and repeat testing. Results: The cohort had a median age of 50 years; 59.2% were male. Upper respiratory tract infections were the most common preceding illness (55.1%). Sensorimotor deficits were the predominant presentation (89.8%), with cranial nerve involvement observed in 28.6% and autonomic dysfunction in 20.4%. Most patients received plasma exchange, and 22.4% required additional immunotherapy. Initial electrophysiological studies were inconclusive in 37.4%, but follow-up improved diagnostic yield to 93.7%. Electrophysiological reclassification occurred in 54.1%, with cases shifting between axonal and demyelinating patterns. At follow-up, AIDP was slightly more prevalent (52%) than axonal forms (41.6%). Functional scores (Hughes, INCAT, MRC) improved significantly within one month, with >65% achieving favorable outcomes. Conclusion: Serial neurophysiological assessment enhances diagnostic accuracy in early GBS subtypes classification, with substantial shifts occurring between initial and follow-up studies. A high proportion of Egyptian GBS patients presented with severe disability but showed marked improvement by the fourth week.
Bayesian Estimation of Spatiotemporal Immune-Viral Dynamics in COVID-19 Using Partial Differential Equations
This study proposes a Bayesian framework for estimating parameters in partial differential equation (PDE) models of viral dynamics. We develop a computational methodology combining Markov Chain Monte Carlo (MCMC) sampling with B-spline basis expansions to address inverse problems in COVID-19 immunology. Applied to clinical data from 30 patients, the model quantifies lymphocyte recruitment kinetics and infection rates during SARS-CoV-2 pathogenesis. Key results demonstrate: (1) mean daily lymphocyte recruitment rate λ̂ = 11.87/day (range: 6.55–14.66), and (2) mean infection rate of pulmonary/lymphoid cells β̂ = 3,556 cells/mL (range: 2,290–5,699). The Bayesian estimator achieved 93.2% posterior coverage probability, confirming its efficacy in characterizing immune response dynamics. These findings provide clinically actionable parameters for optimizing antiviral therapies through precise quantification of host-pathogen interactions. Notably, λ reflects the immune system's capacity to mobilize lymphocytes, with elevated values predicting rapid viral clearance and recovery. In contrast, β serves as a biomarker of viral infectivity severity, where higher values signal increased tissue-level viral load and a greater risk of adverse clinical outcomes.
Alpha Diversity Analysis of Microbiota Dysbiosis in Normal and Colorectal Cancer of Mice Feces
Background: Colorectal cancer development is influenced by both environmental and genetic factors, with the gut microbiota playing a significant role. This research investigates how alterations in gut microbiota are associated with the incidence, progression, prognosis, and early detection of CRC. Methods: An experimental laboratory study was carried out using Sprague Dawley rats that were induced with azoxymethane (AOM) and Dextran Sodium Sulfate (DSS). The thirty rats were divided into three groups: normal, cancer-induced, and treatment. The fecal microbiota profiles were examined through Next Generation Sequencing (NGS), and the data were analyzed for alpha diversity, highlighting the dynamics of the microbial community. Results: The cancer-induced group (K2 Plus) exhibited the highest microbial diversity across Shannon, Simpson, Chao1, and PD Whole Tree indices, while the treatment group (P2 Plus) demonstrated the lowest. Conclusion: These findings suggest that the increase in diversity observed in cancer-induced mice reflects disruption of community stability and blooming of pathobionts. Conversely, treatment with Lactococcus lactis D4 reduced diversity, potentially by selectively suppressing pro-inflammatory or pathogenic taxa, indicating a beneficial probiotic effect in mitigating dysbiosis associated with colorectal cancer.
Bayesian Estimation for Factor Analysis Model in Geriatric Medicine
Bayesian factor analysis has gained prominence in statistical MODELING, particularly in handling parameter uncertainty and small sample sizes. This study presents a Metropolis- Hastings within Gibbs sampling algorithm for estimating a factor analysis model, incorporating Cauchy priors for factor loadings and log-normal priors for residual errors. Unlike traditional approaches, the proposed methodology effectively addresses heavy-tailed distributions in factor loadings and captures the skewness in residual variances. A geriatric dataset comprising 25 items related to locomotive function is used to illustrate the implementation of this Bayesian framework. Model fit is assessed using standard fit indices such as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), and Standardized Root Mean Square Residual (SRMR). The results demonstrate that incorporating non-conjugate priors improves model flexibility and enhances interpretability in factor structure identification. The findings suggest that Cauchy and log-normal priors outperform conventional normal priors in capturing latent structures, providing a robust alternative for Bayesian factor analysis in geriatric research.
Predicting Sex from Hand Dimensions using Statistical Models: A Cross-Sectional Study of Medical Students
Determining sex from dismembered body parts is crucial for forensic and medico-legal investigations. Hand anthropometry offers a practical, non-invasive approach, particularly when utilising statistical models. This study aims to assess sexual dimorphism in hand dimensions and evaluate the effectiveness of statistical methods in determining sex from various hand measurements. Data for this study were obtained from a cross-sectional survey conducted from July to December 2021 involving a sample of 150 undergraduate medical students (78 males, 72 females) aged 18 to 24 years at a private medical college in India. Medical students were selected as a relatively homogeneous population to minimize confounding factors such as occupational variation, lifestyle, and health status, thereby increasing the internal validity of the findings. Measurements of hand length, breadth, and palm length for both the left and right hands were taken. Logistic regression was employed to develop models for sex classification. By using various combinations of explanatory variables, three logistic regression models were fitted to predict sex. Among these models, the one with the best fit was selected as the final model for sex prediction. It was observed that the mean scores of male and female respondents differ significantly. All hand dimensions were significantly larger in males (p < 0.001). According to the best-fitting model, the Right Hand Index (RHI) along with height was identified as the most significant predictors of sex. The best-fitted logistic model achieved 90% accuracy with an AUC of 0.93. Hand dimensions, particularly the Right Hand Indices (RHI), are effective predictors of sex and logistic regression provides a reliable method for forensic identification when complete body parts are unavailable, and this model can be utilised for other types of forensic predictions.
The Table 2 Fallacy and Overfitting: A Persistent Problem in Contemporary Research?
The "Table 2 fallacy" represents a common methodological error in medical research, characterized by indiscriminate statistical adjustment for multiple variables without considering their causal nature. This article examines the theoretical foundations of the problem, distinguishing between studies with descriptive, predictive, and explanatory objectives, and emphasizing how the research purpose should determine the adjustment strategy. We highlight the fundamental role of Directed Acyclic Graphs (DAGs) in correctly identifying confounding, mediating, and colliding variables, thus avoiding overadjustment and resulting biases. To illustrate these considerations, we present two practical examples: the relationship between obesity and colorectal cancer, and between coffee consumption and breast cancer. In the first case, we demonstrate how adjustment for intestinal dysbiosis (a mediator) can attenuate the association between obesity and colorectal cancer, reducing the adjusted relative risk from 1.78 (95% CI: 1.20–2.65) to 1.49 (95% CI: 0.97–2.29) and eliminating statistical significance (p=0.072). In the second example, we show how including insomnia (a collider) in the model can create artificial associations between coffee consumption and breast cancer, dramatically increasing the adjusted relative risk to 1.94 (95% CI: 1.34-2.81) with high statistical significance (p<0.001), when a correctly specified model shows no such association. We conclude that, in explanatory studies, it is essential to develop causal reasoning prior to statistical analysis, using DAGs to guide the selection of adjustment variables. This rigorous methodological approach prevents both the dilution of real causal effects and the generation of spurious associations, increasing the internal validity of epidemiological findings and their utility for clinical decision-making.