Ensemble machine learning prediction and variable importance analysis of 5-year mortality after cardiac valve and CABG operations.

ABSTRACT: Despite having a similar post-operative complication profile, cardiac valve operations are associated with a higher mortality rate compared to coronary artery bypass grafting (CABG) operations. For long-term mortality, few predictors are known. In this study, we applied an ensemble machine learning (ML) algorithm to 88 routinely collected peri-operative variables to predict 5-year mortality after different types of cardiac operations. The Super Learner algorithm was trained using prospectively collected peri-operative data from 8241 patients who underwent cardiac valve, CABG and combined operations. Model performance and calibration were determined for all models, and variable importance analysis was conducted for all peri-operative parameters. Results showed that the predictive accuracy w

SUBMITTER: Castela Forte J 

PROVIDER: S-EPMC7876023 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature