Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction.

ABSTRACT: Machine learning (ML) has been suggested to improve the performance of prediction models. Nevertheless, research on predicting the risk in patients with acute myocardial infarction (AMI) has been limited and showed inconsistency in the performance of ML models versus traditional models (TMs). This study developed ML-based models (logistic regression with regularization, random forest, support vector machine, and extreme gradient boosting) and compared their performance in predicting the short- and long-term mortality of patients with AMI with those of TMs with comparable predictors. The endpoints were the in-hospital mortality of 14,183 participants and the three- and 12-month mortality in patients who survived at discharge. The performance of the ML models in predicting the mortality of p

SUBMITTER: Lee W 

PROVIDER: S-EPMC8213755 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature