Machine learning and SHAP interpretation for predicting coronary heart disease-diabetes comorbidity with dietary antioxidants

Coronary heart disease (CHD) and diabetes mellitus frequently co-occur through shared mechanisms such as oxidative stress and inflammation. Whether specific dietary antioxidants mitigate CHD-diabetes comorbidity remains unclear. Using National Health and Nutrition Examination Survey (NHANES) 2005-2018 data (n = 9,279), we developed an interpretable machine-learning pipeline in which standardisation and Synthetic Minority Over-sampling Technique (SMOTE) were embedded inside each fold of tenfold cross-validation to prevent data leakage. Six algorithms (Random Forest, Light Gradient Boosting Machine (LightGBM), K-nearest neighbours, Naive Bayes, support vector machine, eXtreme Gradient Boosting (XGBoost)) were compared on discrimination, calibration and decision-curve net benefit. XGBoost achieved the highest AUC-ROC (0.774, 95% CI 0.759-0.788); Random Forest showed the lowest Brier score (0.111), the calibration slope closest to unity (0.939) and the highest net benefit, and was retained for interpretation. Weighted-quantile-sum regression showed an inverse association between the antioxidant composite and comorbidity risk (OR per quantile 0.87, 95% CI 0.80-0.95; P = 0.001). In mutually adjusted logistic regression, only magnesium retained an independent protective association (per 1 SD: OR 0.80, 95% CI 0.66-0.96; P = 0.016). SHAP identified theobromine (0.020) and lycopene (0.016) as leading protective contributors. Findings support targeted dietary-antioxidant strategies as candidate modifiable factors for cardiometabolic comorbidity prevention.