Enhanced machine learning and hybrid ensemble approaches for Coronary Heart Disease prediction.

ABSTRACT: Coronary heart disease (CHD) remains the leading cause of mortality worldwide, disproportionately affecting low- and middle-income countries where diagnostic resources are limited. Traditional statistical models often fail to deliver adequate predictive accuracy in complex, high-dimensional, and imbalanced health datasets. To develop and evaluate enhanced machine learning and hybrid ensemble models for the prediction of coronary heart disease, with a focus on improving diagnostic performance, interpretability, and applicability in resource-constrained settings. We utilized a nationally representative dataset of 253,680 individuals from the Behavioral Risk Factor Surveillance System. Preprocessing included normalization and balancing via the Synthetic Minority Oversampling Technique (SMOTE)

SUBMITTER: Wanyonyi M 

PROVIDER: S-EPMC12742797 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature