Robust prognostic prediction model developed with integrated biological markers for acute myocardial infarction

ABSTRACT: Commonly used prediction methods for acute myocardial infarction (AMI) were created before contemporary percutaneous coronary intervention was recognized as the primary therapy. Although several studies have used machine learning techniques for prognostic prediction of patients with AMI, its clinical application has not been achieved. Here, we developed an online application tool using a machine learning model to predict in-hospital mortality in patients with AMI. A total of 2,553 cases of ST-elevation AMI were assigned to 80% training subset for cross validation and 20% test subset for model performance evaluation. We implemented random forest classifier for the binary classification of in-hospital mortality. The selected best feature set consisted of ten clinical and biological markers i

SUBMITTER: Nishi M 

PROVIDER: S-EPMC9632913 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature