Development and validation of a machine learning model for prediction of 1-year mortality following ST-elevation myocardial infarction: a retrospective cohort study.

ABSTRACT:

Objectives

To develop a machine learning (ML)-based risk prediction model for 1-year mortality in ST-elevation myocardial infarction (STEMI) patients undergoing primary or rescue percutaneous coronary intervention.

Design

Patient data, including demographic, clinical, biochemical, imaging and procedural details, were extracted from electronic medical records. Data were split into training (80%) and test (20%) sets. Eight supervised learning algorithms were evaluated: least absolute shrinkage and selection operator, ridge, Elastic Net (EN, decision tree, support vector machine, random forest, AdaBoost and gradient boosting. Feature selection was performed sequentially with subsets of the top 5/10/15/20/25/30 features. Model hyperparameters were optimised using fivefold cross-

SUBMITTER: Sritharan HP 

PROVIDER: S-EPMC12911757 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature