Interpretable machine learning models for the prediction of all-cause mortality and time to death in hemodialysis patients.

ABSTRACT:

Introduction

The elevated mortality and hospitalization rates among hemodialysis (HD) patients underscore the necessity for the development of accurate predictive tools. This study developed two models for predicting all-cause mortality and time to death-one using a comprehensive database and another simpler model based on demographic and clinical data without laboratory tests.

Method

A retrospective cohort study was conducted from January 2017 to June 2023. Two models were created: Model A with 85 variables and Model B with 22 variables. We assessed the models using random forest (RF), support vector machine, and logistic regression, comparing their performance via the AU-ROC. The RF regression model was used to predict time to death. To identify the most relevant factors f

SUBMITTER: Chen M 

PROVIDER: S-EPMC11879476 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature