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

cs.LGAug 17, 2026

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

Authors: Md. Atik Shams, David Eisenberg, Sumaiya Fatema, Asma Sultana, D. M Hasibul Islam, Junnatul Mawa, Anindita Datta, Nafiya Ahmed,

Organizations: Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1205, Bangladesh · Department of Computer Science and Engineering, Notre Dame University Bangladesh, Dhaka 1000, Bangladesh · Department of Information Management and Business Analytics, Montclair State University, Feliciano School of Business, NJ, USA · Department of Fish and Wildlife Conservation, College of Natural Resources and Environment, Virginia Tech, Blacksburg, VA 24060, USA · Department of Computer Science, BRAC University, Dhaka 1212, Bangladesh · Department of Computer Science and Engineering, Northern University Bangladesh, Dhaka 1230, Bangladesh · Institute of Biological Sciences, Rajshahi University, Rajshahi 6205, Bangladesh · Martin Tuchman School of Management, New Jersey Institute of Technology, Newark, 07102, NJ, USA · Institute for Population and Precision Health, University of Chicago, Department of Family Medicine, Illinois, USA · Department of Computer Science, College of Engineering, Virginia Tech, Blacksburg, VA 24060, USA · School of Medicine, Stanford University, Stanford, CA 94305, USA

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.

Clinical Risk Prediction

Clinical risk prediction estimates patients’ future health outcomes from medical records, physiological signals, imaging, and other health data.

See all 218 papers

Evaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability
Jul 12, 2026Mashrul Hossain, Nafesa Kibria, Fahim ShahriarData Leakage Clinical Prediction
East West University, Dhaka, Bangladesh
LLM4CKD: Large Language Models for Early Stage Chronic Kidney Disease Screening
Sep 3, 2026Muhammad Ashad Kabir, Sirajam MuniraMedical Diagnosis In-Context Learning
School of Computing, Mathematics and Engineering Charles Sturt University NSW, Australia · Department of Computer Science Rensselaer Polytechnic Institute NY, USA
From Many to Meaningful: Feature-Guided Zero-Shot Chronic Kidney Disease Screening Using Large Language Models
Jul 14, 2026Muhammad Ashad Kabir, Sirajam MuniraZero-Shot Learning Medical Diagnosis
School of Computing, Mathematics and Engineering, Charles Sturt University, NSW, Australia · Department of Computer Science, Rensselaer Polytechnic Institute, NY, USA

Jul 12, 2026J/K move · Enter open · S save

Evaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability

Mashrul Hossain, Nafesa Kibria, Fahim Shahriar

East West University, Dhaka, Bangladesh

Data Leakage Clinical Prediction Scientific Reproducibility Clinical Risk Prediction

Open paper AskPDF arXiv

The early detection of Chronic Kidney Disease using machine learning has attracted significant interest in healthcare-related computer science. Despite rapid advancements in this field, many reported studies remain inconsistent and potentially misleading. A significant drawback is the lack of organized evaluation regarding methodological concerns. Key issues include data leakage, limited access to temporal patient records and inconsistency in reported clinical indicators. This research offers a systematic literature review of existing CKD prediction studies using interpretable machine learning techniques, where nineteen relevant studies were selected via systematic searches across major academic databases. To assess methodological reliability, this study introduces a structured taxonomy of information leakage and a quantitative leakage scoring framework to systematically evaluate reliability across CKD prediction studies. The analysis reveals a strong relationship between leakage and inflated performance. Here, High leakage-studies report an average accuracy of 95.48%, compared to 80.2% for leakage-free studies, reflecting an increase of approximately 15.28%. Furthermore, a cross-study feature stability analysis shows that only a small subset of predictors is consistently reproducible, with over 80% lacking reliability. Overall, the findings suggest that many reported performance improvements stem from methodological limitations rather than true predictive capability.

Abstract

Clinical Risk Prediction

Explore similar work

Evaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability

LLM4CKD: Large Language Models for Early Stage Chronic Kidney Disease Screening

From Many to Meaningful: Feature-Guided Zero-Shot Chronic Kidney Disease Screening Using Large Language Models