Machine learning to predict postdialysis fatigue in patients undergoing hemodialysis.

ABSTRACT:

Background

Machine learning (ML) has been widely used to predict complications and prognosis in patients undergoing hemodialysis (HD). However, accurate and efficient models for predicting postdialysis fatigue (PDF) in this population are still needed because PDF is surprisingly prevalent.

Aims

This study aimed to explore the potential of ML models for predicting PDF in patients undergoing HD.

Data sources

A total of 1,281 Chinese patients undergoing HD from six tertiary hospitals (65.26% male, mean age = 54.48 years).

Design

Cross-sectional study.

Methods

Seven ML models were compared: Logistic regression (LR), Decision tree (DT), Random forests (RF), LightGBM (LGBM), CatBoost, XGBoost (XGB), and Gradient boosting tree (GBT), to predict the PDF and iden

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12302430 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature