AI-Powered Predictive Analytics for Cardiovascular Disease Risk Assessment

Cardiovascular disease (CVD) remains a leading cause of global morbidity, underscoring the need for accurate and timely risk assessment frameworks. This study explores an AI-powered predictive analytics model that estimates individual CVD risk using multimodal clinical data, including demographic factors, laboratory values, lifestyle indicators, and historical medical records. The proposed system integrates machine learning classifiers with feature-selection strategies to identify influential risk determinants while maintaining model transparency. Experimental evaluations demonstrate that the AI model improves prediction accuracy compared with traditional statistical approaches, particularly in handling complex, non-linear patterns in patient data. The study also highlights the model’s potential for early risk stratification and personalized preventive care. These findings suggest that AI-driven analytics can support clinicians in making informed decisions and enhancing population-level cardiovascular health management.