Advanced Machine Learning to Predict Coronary Artery Disease Severity in Patients with Premature Myocardial Infarction.

ABSTRACT:

Background

Studies using machine learning to identify the target characteristics and develop predictive models for coronary artery disease severity in patients with premature myocardial infarction (PMI) are limited.

Methods

In this observational study, 1111 PMI patients (≤55 years) at Tianjin Chest Hospital from 2017 to 2022 were selected and divided according to their SYNTAX scores into a low-risk group (≤22) and medium-high-risk group (>22). These groups were further randomly assigned to a training or test set in a ratio of 7:3. Lasso-logistic was initially used to screen out target factors. Subsequently, Lasso-logistic, random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) were used to establish prediction mode

SUBMITTER: Wang YH 

PROVIDER: S-EPMC11760553 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature