A biomarker discovery of acute myocardial infarction using feature selection and machine learning.

ABSTRACT: Acute myocardial infarction (AMI) or heart attack is a significant global health threat and one of the leading causes of death. The evolution of machine learning has greatly revamped the risk stratification and death prediction of AMI. In this study, an integrated feature selection and machine learning approach was used to identify potential biomarkers for early detection and treatment of AMI. First, feature selection was conducted and evaluated before all classification tasks with machine learning. Full classification models (using all 62 features) and reduced classification models (using various feature selection methods ranging from 5 to 30 features) were built and evaluated using six machine learning classification algorithms. The results showed that the reduced models performed genera

SUBMITTER: Mohd Faizal AS 

PROVIDER: S-EPMC10191821 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature