Coronary heart disease risk prediction based on GAIN imputation and interpretable machine learning

The developed model demonstrates robust prediction performance combined with high clinical interpretability. Unlike traditional "black box" models, this approach clarifies the contribution of specific risk factors. Crucially, the tool is well-suited for dual deployment: serving as an automated screening tool integrated into hospital electronic health records (EHRs) and as a self-monitoring aid for individuals with underlying health conditions via mobile health applications.