LightGBM Predicts Coronary Risk in HHcy
Researchers at the First Affiliated Hospital of Henan University of Traditional Chinese Medicine conducted a single-center retrospective study (Jan 1, 2022–July 1, 2025) developing seven machine-learning models to predict coronary heart disease (CHD) risk in patients with hyperhomocysteinemia (HHcy). The LightGBM model performed best on the test set (AUC=0.807, F1=0.606, Brier=0.2415) using six routinely available variables. SHAP analysis highlighted age and activated partial thromboplastin time as top predictors, suggesting an interpretable tool for early risk stratification.
Scoring Rationale
Interpretable model with solid metrics and clinical relevance, limited by single-center retrospective design needing external validation.
Practice interview problems based on real data
1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with.
Try 250 free problems
