Interpretable machine learning for coronary heart disease risk stratification in patients with carotid atherosclerosis: A retrospective cross-sectional study.

ABSTRACT: This study aimed to develop and validate a machine learning model for risk stratification of coronary heart disease (CHD) in patients with carotid atherosclerosis, with CHD presence/absence defined as the target outcome variable. A retrospective analysis was conducted on 442 patients diagnosed with carotid atherosclerosis at a tertiary hospital in China between January 1, 2022, and June 20, 2025. Patients were divided into CHD and non-CHD groups based on clinical outcomes. Data encompassing demographics, laboratory results, and vascular imaging findings were collected. Feature selection involved logistic regression (LR), identifying 5 key predictors: age, diabetes, hyperlipidemia, transient ischemic attack (TIA), and the presence of carotid atherosclerotic plaque. Seven machine learning al

SUBMITTER: Zhang L 

PROVIDER: S-EPMC12826324 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature