Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients.

ABSTRACT:

Background

Risk stratification is an integral component of ST-segment-elevation myocardial infarction (STEMI) management practices. This study aimed to derive a machine learning (ML) model for risk stratification and identification of factors associated with in-hospital and 30-day mortality in patients with STEMI and compare it with traditional TIMI score.

Methods

This was a single center prospective study wherein subjects >18 years with STEMI (n = 1700) were enrolled. Patients were divided into two groups: training (n = 1360) and validation dataset (n = 340). Six ML algorithms (Extra Tree, Random Forest, Multiple Perceptron, CatBoost, Logistic Regression and XGBoost) were used to train and tune the ML model and to determine the predictors of worse outcomes using feature sel

SUBMITTER: Gupta MD 

PROVIDER: S-EPMC12223519 | biostudies-literature | 2025 May-Jun

REPOSITORIES: biostudies-literature