Population Health-Based Machine Learning Reveals Associations Between Psychosocial Factors and Chronic Kidney Disease

Md. Atik Shams; David Eisenberg; Sumaiya Fatema; Asma Sultana; D. M Hasibul Islam; Junnatul Mawa; Anindita Datta; Nafiya Ahmed; Danastan Tasaouf Mridula; SK. Sazid Mahmud; Simon Bin Akter; Tanjila Helaly; Jorge Fresneda Fernandez; Humayera Islam; Tanmoy Sarkar Pias

arXiv · 2608.17174

Population Health-Based Machine Learning Reveals Associations Between Psychosocial Factors and Chronic Kidney Disease

Md. Atik Shams·David Eisenberg·Sumaiya Fatema·Asma Sultana·D. M Hasibul Islam·Junnatul Mawa·Anindita Datta·Nafiya Ahmed·Danastan Tasaouf Mridula·SK. Sazid Mahmud·Simon Bin Akter·Tanjila Helaly·Jorge Fresneda Fernandez·Humayera Islam·Tanmoy Sarkar Pias

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Abstract

Chronic kidney disease (CKD) progresses silently and severely undermines quality of life, making early detection critical for improving patient outcomes. We present a two-part study that combines large-scale telehealth data with advanced machine learning to both classify self-reported CKD status and identify key drivers of disease. Using selected features from the Behavioral Risk Factor Surveillance System (BRFSS 2021: 438,693 samples; BRFSS 2019: 418,268 samples) and the National Health Interview Survey (NHIS 2021: 29,482 samples; NHIS 2020: 31,568 samples), we addressed missing data with nine state-of-the-art imputation methods and mitigated class imbalance via sampling strategies. Our customized stacked ensemble model achieved balanced accuracy of 72.56-76.12%, with corresponding AUROC scores of 79.59-82.29%. SHapley Additive exPlanations (SHAP) analysis, followed by clinical review, highlighted critical predictors, including regular medical check-ups, age, blood pressure, and indicators of mental health stress. These findings deliver a robust and interpretable framework for CKD risk stratification and provide actionable insights into its associated factors.

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Md. Atik Shams, David Eisenberg, Sumaiya Fatema, Asma Sultana, D. M Hasibul Islam, Junnatul Mawa, Anindita Datta, Nafiya Ahmed, Danastan Tasaouf Mridula, SK. Sazid Mahmud, Simon Bin Akter, Tanjila Helaly, Jorge Fresneda Fernandez, Humayera Islam, Tanmoy Sarkar Pias. 2026-08-17. Population Health-Based Machine Learning Reveals Associations Between Psychosocial Factors and Chronic Kidney Disease. https://arxiv.org/abs/2608.17174

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