Harnessing Clinical and Biochemical Data for Personalized Cardiovascular Risk Prediction: a Machine Learning Approach Toward Precision Nutrition

ML models-particularly Random Forest and XGBoost-demonstrated high accuracy and interpretability in predicting CVD risk among rural postmenopausal women. These findings highlight the potential of AI-driven, low-cost predictive tools for early CVD risk detection and personalized preventive healthcare in resource-limited rural settings.