AI-Enhanced Predictive Modeling for Cardiovascular Disease Risk Assessment

Abstract

Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide. This study explores an AI-driven predictive modeling approach to assess CVD risk based on patient demographics, medical history, and real-time physiological data. Using machine learning techniques such as gradient boosting and deep neural networks, the model identifies high-risk individuals and suggests personalized preventive strategies. The proposed framework demonstrates high predictive accuracy, offering a scalable solution for early cardiovascular risk assessment and intervention.