AI Enabled Cardiovascular Disease Risk Prediction through Multimodal Data Fusion: A Predictive Analytics Approach

Abstract

This study presents a comprehensive framework for cardiovascular disease risk prediction utilizing artificial intelligence-enhanced multimodal data fusion techniques. The proposed approach integrates diverse data modalities including electrocardiographic signals, hemodynamic parameters, laboratory biomarkers, and clinical phenotypes through an adaptive attention-based fusion architecture. Our methodology employs ensemble learning algorithms combined with deep neural networks to construct robust predictive models capable of identifying high-risk populations with superior accuracy compared to traditional risk assessment tools. The framework incorporates advanced feature extraction mechanisms, temporal synchronization protocols, and uncertainty quantification methods to enhance clinical interpretability. Experimental validation demonstrates significant improvements in risk stratification performance, achieving area under curve values exceeding 0.92 across multiple cardiovascular endpoints. The integration of real-time monitoring capabilities with personalized risk profiling enables dynamic assessment of cardiovascular health status, supporting precision medicine initiatives in preventive cardiology. This research contributes to the advancement of intelligent healthcare systems by providing clinicians with enhanced decision-support tools for early intervention strategies and optimized resource allocation in cardiovascular disease management.