arXiv Machine Learning

Machine Learning-Based Prediction of Childhood Stunting in Bangladesh: Fairness and Temporal Robustness Assessment

arXiv Machine Learning

Jun 16

Temporal Validation Changes the Apparent Public-Health Utility of Under-Five Mortality Prediction in Bangladesh: A Four-Round DHS Machine-Learning Study

arXiv:2602. 03957v2 Announce Type: replace Abstract: Background: Under-five mortality in Bangladesh remains uneven despite national progress.

By Md Muhtasim Munif Fahim, M. Monimul Huq, M. Sabiruzzaman, Md Rezaul Karim

arXiv Machine Learning

Jul 28

Deep learning approaches show promise for predicting childhood malnutrition: A comparative study with traditional machine learning methods using survey data

arXiv:2602. 10381v2 Announce Type: replace Abstract: Childhood malnutrition remains a major public health concern in Nepal and other low-resource settings, while conventional case-finding approaches are labor-intensive and frequently unavailable in remote areas.

By Deepak Bastola, Yang Li

arXiv Machine Learning

Jul 21

Interpretable Machine Learning for Air Pollution and Respiratory Health Prediction: A Socioeconomic Subgroup Analysis

arXiv:2607. 17024v1 Announce Type: new Abstract: Air pollution and climate-related stressors are increasingly important concerns for respiratory health, especially in settings with unequal environmental exposure and healthcare capacity.

By Maede Azani Hassan Abadi, Shouyi Wang

arXiv AI

Jul 21

Comprehensive Evaluation of Machine Learning for Type 2 Diabetes Risk Prediction: Large-Scale External Validation and Fairness Analysis

arXiv:2607. 16253v1 Announce Type: cross Abstract: Machine learning-based Type 2 diabetes risk prediction models obtain good internal validation results but lose effectiveness in real-world applications due to deficient external testing and fairness assessment.

By Rajveer Singh Pall, Sameer Yadav, Siddharth Bhalerao, Sourabh Sahu, Ritu Ahluwalia, Bhaskar Awadhiya

safety

arXiv Machine Learning

Jun 2

Early Prediction of Liver Cirrhosis Up to Two Years in Advance: A Machine Learning Study Benchmarking Against the FIB-4 and APRI Scores

arXiv:2601. 00175v2 Announce Type: replace Abstract: Objective: Develop and evaluate machine learning (ML) models for predicting incident liver cirrhosis (LC) one and two years prior to diagnosis using routinely collected electronic health record (EHR) data and benchmark their performance against the FIB-4 and APRI clinical scores.

By Zhuqi Miao, Ahmed G Qasem, Sujan Ravi, Jason T. Cheng, Abdulaziz Ahmed, Courtney W. Houchen, Sumayah Abed, Dilorom Azimdjanovna Zuparova, Abdulaziz Ahmed

benchmarks

arXiv Machine Learning

Aug 24

Machine Learning and ARIMA Model Averaging for Adaptive Public Health Forecasting: Comparative Evaluation and an Ontario COVID-19 Case Study

arXiv:2608.20406v1 Announce Type: new Abstract: Public health forecasts must respond to abrupt changes in surveillance data without over-extrapolating noise, reporting artifacts, or temporary trends....

By Yushu Zou, Ye Li, Johra Moosa, Martin Grunnill, Samir N. Patel, Venkata R. Duvvuri

arXiv Machine Learning

Sep 18

Machine-Learning Assessment of the Predictive Value of Inflammatory Biomarkers for Cognitive Impairment in an Older Hispanic Adult Cohort

The study evaluates whether inflammatory biomarkers can predict cognitive impairment in older Hispanic adults using interpretable machine learning on a small clinical dataset. A leakage‑safe Bernoulli/Categorical Naive Bayes model was trained on 165 participants from the Panama Aging Research Initiative, with continuous predictors discretized via supervised chi‑square and income treated categorically. The biomarker I‑309 (CCL1) emerged as the sole reliable incremental predictor, boosting ROC‑AUC from 0.630 to 0.740 and achieving statistically significant performance across repeated cross‑validation and random partitions.

By Antony Garcia, Gabrielle Britton, Alcibiades Villarreal, Diana Oviedo, Giselle Rangel, Xinming Huang

arXiv Machine Learning

Aug 19

Predicting Male Domestic Violence Using Explainable Ensemble Learning and Exploratory Data Analysis

The paper presents a data‑driven study of male domestic violence (MDV) in Bangladesh, using exploratory data analysis to uncover patterns such as verbal abuse prevalence and the influence of financial dependency. It evaluates 10 traditional ML models, 3 deep learning models, and 2 ensemble models, ultimately proposing a stacking ensemble with ANN and CatBoost base classifiers and Logistic Regression meta‑model that achieves 95% accuracy and 99.29% AUC. Explainable AI techniques (SHAP, LIME) and statistical validation confirm the model’s superior performance and highlight key features driving predictions.

By Md Abrar Jahin, Saleh Akram Naife, Fatema Tuj Johora Lima, M. F. Mridha, Md. Jakir Hossen

safety

arXiv AI

Jul 7

Explainable AI for Screening Abuse-Related Trauma in Bangladeshi Children: A Training-Free Multimodal Framework Evaluated on Noise-Aware Synthetic Data

arXiv:2607. 04010v1 Announce Type: new Abstract: Bangladesh has an estimated 1.

By Salma Hoque Talukdar Koli, Fahima Haque Talukder Jely

multimodal benchmarks

arXiv Machine Learning

Jun 9

QDSP: An Interpretable Structured Learning Framework for Predicting Death or Cerebral Palsy in Very Low Birth Weight Infants

arXiv:2606. 07606v1 Announce Type: new Abstract: Very low birth weight infants (VLBWI) are at high risk of mortality and severe neurodevelopmental impairment, including cerebral palsy, yet reliable discharge-time prognostic stratification remains challenging in high-dimensional and data-limited clinical settings.

By Ling Wang, Xiaolong Li, Hui Zhou, Jing Shi, Fuhao Zhang, Dapeng Chen, Nan Mu

arXiv Machine Learning

4d ago

Does Machine Learning Outperform Traditional Fibrosis Scores in Predicting Liver Cirrhosis Risk? A Longitudinal EHR-Based Study

arXiv:2601.00175v3 Announce Type: replace Abstract: Objective: Develop and evaluate machine learning (ML) models for predicting incident liver cirrhosis (LC) one and two years before diagnosis using...

By Zhuqi Miao, Ahmed G Qasem, Sujan Ravi, Jason T. Cheng, Abdulaziz Ahmed, Courtney W. Houchen, Sumayah Abed, Dilorom Azimdjanovna Zuparova, Abdulaziz Ahmed

arXiv Machine Learning

Aug 3

Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence

arXiv:2607. 29456v1 Announce Type: cross Abstract: Double Machine Learning (DML) is a popular approach for treatment effect estimation in various settings, which allows a wide range of flexible machine learning methods to be used for nuisance parameter estimation while preserving valid inference.

By Haozheng Xu, Siyuan Ma, Qingyan Xiang

safety

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