Huzhou Central Hospital (Zhejiang Chinese Medical University / Huzhou University)
Model weights not public. Contact creators for more information.
Interpretable coronary heart disease (CHD) risk prediction model based on the LightGBM gradient-boosting algorithm, combined with SHAP (SHapley Additive exPlanations) values to make individual risk predictions explainable to clinicians. Trained on the public BRFSS_2015 survey dataset and externally validated on the Framingham and Z-Alizadeh Sani datasets, the model reached 90.60% accuracy and 81.06% AUROC on BRFSS_2015, with SHAP analysis identifying age, smoking status, diabetes, hypertension, and high cholesterol as the most influential risk features. A companion CHD scoring system was built from the model to give clinicians a user-friendly risk-assessment tool.
Architecture
Other
Gradient-boosted decision tree ensemble (LightGBM) over structured clinical/behavioral risk-factor features, with SHAP values used for post-hoc interpretability
Added to catalog
2026-08-14
BRFSS 2015 (CHD risk subset)
Framingham Heart Study (LightGBM CHD external validation subset)
Z-Alizadeh Sani Dataset
Binary risk prediction of coronary heart disease from structured clinical/behavioral risk factors, with a derived CHD risk score