Use of machine learning models to predict mortality in dialysis patients.

ABSTRACT:

Background

Mortality among maintenance hemodialysis patients remains high, and traditional statistical models often fail to capture complex clinical relationships. This study aimed to systematically develop, compare, and validate 19 machine learning algorithms for predicting all-cause mortality in maintenance hemodialysis patients.

Methods

This retrospective study included data from 538 maintenance hemodialysis patients (2018.1-2023.12), with 70% used for training and 30% for testing. Each model underwent hyperparameter optimization based on three performance metrics (accuracy, F1-score, and ROC Area Under the Curve [AUC]) to evaluate the impact of different clinical priorities.

Results

Gradient boosting models demonstrated consistent superiority, with performance out

SUBMITTER: Huang J 

PROVIDER: S-EPMC12714995 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature