Predicting low density lipoprotein cholesterol target attainment using machine learning in patients with coronary artery disease receiving moderate-dose statin therapy.

ABSTRACT: Low-density lipoprotein cholesterol (LDL-C) is an important factor in the development of cardiovascular disease, making its management a key aspect of cardiovascular health. While high-dose statin therapy is often recommended for LDL-C reduction, careful consideration is needed due to patient-specific factors and potential side effects. This study aimed to develop a machine learning (ML) model to estimate the likelihood of achieving target LDL-C levels in patients hospitalized for coronary artery disease and treated with moderate-dose statins. The predictive performance of three ML models, including Extreme Gradient Boosting (XGBoost), Random Forest, and Logistic Regression, was evaluated using electronic medical records from the Asan Medical Center in Seoul across six performance metrics.

SUBMITTER: Han J 

PROVIDER: S-EPMC11825908 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature