Machine learning and SHAP value interpretation for predicting cardiovascular disease risk in patients with diabetes using dietary antioxidants.

Objective: This study aims to develop and validate a machine learning model that integrates dietary antioxidants to predict cardiovascular disease (CVD) risk in diabetic patients. By analyzing the contributions of key antioxidants using SHAP values, the study offers evidence-based insights and dietary recommendations to improve cardiovascular health in diabetic individuals. Methods: This study leveraged data from the U.S. National Health and Nutrition Examination Survey (NHANES) to develop predictive models incorporating antioxidant-related variables-including vitamins, minerals, and polyphenols-alongside demographic, lifestyle, and health status factors. Data preprocessing involved collinearity removal, standardization, and class imbalance correction. Multiple machine learning models were developed and evaluated using the mlr3 framework, with benchmark testing performed to compare predictive performance. Feature importance in the best-performing model was interpreted using SHapley Additive exPlanations (SHAP). Results: This study utilized data from 1,356 individuals with diabetes from NHANES, including 332 with comorbid CVD. After removing collinear variables, 27 dietary antioxidant features and 13 baseline covariates were retained. Among all models, XGBoost demonstrated the best predictive performance, with an accuracy of 87.4%, an error rate of 12.6%, and both AUC and PRC values of 0.949. SHAP analysis highlighted Daidzein, magnesium (Mg), epigallocatechin-3-gallate (EGCG), pelargonidin, vitamin A, and theaflavin 3'-gallate as the most influential predictors. Conclusion: XGBoost exhibited the highest predictive performance for cardiovascular disease risk in diabetic patients. SHAP analysis underscored the prominent contribution of dietary antioxidants, with Daidzein and Mg emerging as the most influential predictors.