{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["25(1)"],"submitter":["Yan Q"],"pubmed_abstract":["<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 enr"],"journal":["BMC medical informatics and decision making"],"pagination":["172"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12016290"],"repository":["biostudies-literature"],"pubmed_title":["Explainable machine learning algorithm to predict cardiovascular event in patients undergoing peritoneal dialysis."],"pmcid":["PMC12016290"],"pubmed_authors":["Yan Q","Wang R","Li D","Wang D","Chen X","Cong J","Liu G"],"additional_accession":[]},"is_claimable":false,"name":"Explainable machine learning algorithm to predict cardiovascular event in patients undergoing peritoneal dialysis.","description":"<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 enr","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Apr","modification":"2025-07-02T03:05:45.286Z","creation":"2025-07-02T03:05:45.286Z"},"accession":"S-EPMC12016290","cross_references":{"pubmed":["40264140"],"doi":["10.1186/s12911-025-03003-w"]}}