biostudies-literatureNi PScience and Technology Development Project of HangzhouKey Program Cosponsored by Zhejiang Province and National Health Commission of ChinaConstruction Fund of Medical Key Disciplines of Hangzhou8691https://www.ebi.ac.uk/biostudies/studies/S-EPMC11907063biostudies-literatureUnknown15(1)Cardiac arrest (CA) poses a significant global health challenge and often results in poor prognosis. We developed an interpretable and applicable machine learning (ML) model for predicting in-hospital mortality of CA patients who survived more than 72 h. A total of 721 patients were extracted from the Medical Information Mart for Intensive Care IV database, divided into the training set (n = 576) and the internal validation set (n = 145). The external validation set containing 856 cases were collected from four tertiary hospitals in Zhejiang Province. The primary outcome was in-hospital mortality. Eleven ML algorithms were utilized to establish prediction models based on data from 72 h after return of spontaneous circulation (ROSC). The results indicate that the CatBoost model exhibited thScientific reportsDevelopment and validation of machine learning-based prediction model for outcome of cardiac arrest in intensive care units.PMC11907063202204A10OO20200485WKJ-ZJ-2315Zhang GZhang WZhu YHu WZhang HZhang SNi PDiao MfalseDevelopment and validation of machine learning-based prediction model for outcome of cardiac arrest in intensive care units.Cardiac arrest (CA) poses a significant global health challenge and often results in poor prognosis. We developed an interpretable and applicable machine learning (ML) model for predicting in-hospital mortality of CA patients who survived more than 72 h. A total of 721 patients were extracted from the Medical Information Mart for Intensive Care IV database, divided into the training set (n = 576) and the internal validation set (n = 145). The external validation set containing 856 cases were collected from four tertiary hospitals in Zhejiang Province. The primary outcome was in-hospital mortality. Eleven ML algorithms were utilized to establish prediction models based on data from 72 h after return of spontaneous circulation (ROSC). The results indicate that the CatBoost model exhibited th2025-01-01T00:00:00Z2025 Mar2025-04-05T22:07:58.595Z2025-04-05T22:07:58.595ZS-EPMC119070634008256910.1038/s41598-025-93182-3