arXiv Machine Learning August 19, 2026 By 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

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

Read the original on arXiv Machine Learning →

The study uses large-scale telehealth data and machine learning to classify self‑reported chronic kidney disease (CKD) status and identify key risk factors. A customized stacked ensemble model achieved balanced accuracy of 72.56–76.12% and AUROC of 79.59–82.29%. SHapley Additive exPlanations revealed that regular medical check‑ups, age, blood pressure, and mental health stress indicators are critical predictors of CKD.

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