Abstract
Prediction models were developed to assess the risk of cardiovascular disease (CVD) based on micronutrient intake, utilizing data from 90,167 UK Biobank participants. Four machine learning models were employed to predict CVD risk, with performance evaluation metrics including area under the receiver operating characteristic curve (AUC), accuracy, recall, specificity, and F1-score. The eXtreme Gradient Boosting (XGBoost) model was utilized to rank the importance of 11 micronutrients in cardiovascular health. Results indicated that vitamin E, calcium, vitamin C, and potassium intake were associated with a reduced risk of CVD. The XGBoost model demonstrated the highest performance with an AUC of 0.952, highlighting potassium, vitamin E, and vitamin C as key predictors of CVD risk. Subgroup analysis revealed a stronger correlation between calcium intake and CVD risk in older adults and those with higher BMI, while vitamin B6 intake showed a link to CVD risk in women. Overall, the XGBoost model emphasized the significance of potassium, vitamin E, and vitamin C intake as primary predictors of CVD risk in adults, with age, sex, and BMI potentially influencing the importance of micronutrient intake in predicting CVD risk.