Explainable machine learning algorithm to predict cardiovascular event in patients undergoing peritoneal dialysis.

ABSTRACT:

Objective

To compare the performance of predictive models for cardiovascular event (CVE) in patients undergoing peritoneal dialysis (PD) based on machine learning algorithm and Cox proportional hazard regression.

Methods

This study included patients underwent PD catheterization in our center from January 1, 2010, to July 31, 2022. The patients were randomly divided into training and validation sets in a 7:3 ratio. Cox regression, extreme gradient boosting (XGBoost), and random survival forest (RSF) models were developed using the training set and validated using the validation set. The time-dependent area under the curve (AUC) and concordance index (C-index) were used to evaluate the discriminative ability of predictive models.

Results

A total of 318 patients were enr

SUBMITTER: Yan Q 

PROVIDER: S-EPMC12016290 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature