The Prediction of All-Cause Mortality in End-Stage Kidney Disease Patients using Social Determinants of Health: A Machine Learning Framework

Authors

• Addy Smith, South Dakota State University
• Maxwell Donelan, South Dakota State University
• Hossein Moradi Rekabdarkolaee, Bowling Green State University
• Patti Brooks, Dakota State University
• Brandon Varilek, University of Nebraska Medical CenterFollow
• Surachat Ngorsuraches, Auburn University
• Jerry Schrier, Avera McKennan Hospital & University Health Center
• Adam Dell, University of South Dakota
• Semhar Michael, South Dakota State University

Document Type

Article

Journal Title

Informatics in Medicine Unlocked

Publication Date

2026

Volume

64

Abstract

End-stage kidney disease (ESKD) is the irreversible final stage of chronic kidney disease in which the kidneys lose their independent function. This study presents a machine learning framework to predict all-cause mortality in ESKD patients by the end of a follow-up period. We combined patient-specific clinical factors with social determinants of health (SDOH) to assess their influence on survival outcomes. Data were obtained from the United States Renal Data System, including patients admitted in 2015 and followed through August 2021. Community-level SDOH data were integrated from the Agency for Healthcare Research and Quality, with variable screening techniques and expert input guiding feature selection. To address class imbalance, the synthetic minority oversampling technique (SMOTE) was applied. Three machine learning models were developed: logistic regression, random forest, and extreme gradient boosting. Model performance was assessed using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Model calibration was assessed using a calibration curve and a Brier score. The extreme gradient boosting model performed best, with an AUC of 0.7947, although other models showed comparable results. Including community-level SDOH features did not significantly improve model performance overall or within subpopulations. This suggests patient-level variables are the primary drivers of mortality prediction in ESKD. Furthermore, SMOTE did not enhance model performance in subpopulations.

DOI Link

10.1016/j.imu.2026.101779

ISSN

2352-9148

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Smith, Addy; Donelan, Maxwell; Rekabdarkolaee, Hossein Moradi; Brooks, Patti; Varilek, Brandon; Ngorsuraches, Surachat; Schrier, Jerry; Dell, Adam; and Michael, Semhar, "The Prediction of All-Cause Mortality in End-Stage Kidney Disease Patients using Social Determinants of Health: A Machine Learning Framework" (2026). Journal Articles: College of Nursing. 34.
https://digitalcommons.unmc.edu/con_articles/34