biostudies-literatureUnknown25(1)Yan Q<h4>Objective</h4>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.<h4>Methods</h4>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.<h4>Results</h4>A total of 318 patients were enrBMC medical informatics and decision making172https://www.ebi.ac.uk/biostudies/studies/S-EPMC12016290biostudies-literatureExplainable machine learning algorithm to predict cardiovascular event in patients undergoing peritoneal dialysis.PMC12016290Yan QWang RLi DWang DChen XCong JLiu GfalseExplainable machine learning algorithm to predict cardiovascular event in patients undergoing peritoneal dialysis.<h4>Objective</h4>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.<h4>Methods</h4>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.<h4>Results</h4>A total of 318 patients were enr2025-01-01T00:00:00Z2025 Apr2025-07-02T03:05:45.286Z2025-07-02T03:05:45.286ZS-EPMC120162904026414010.1186/s12911-025-03003-w