Published Paper Details:

Cardiovascular diseases (CVDs) remain a leading cause of mortality globally, necessitating effective risk prediction models for early identification and intervention. In recent years, artificial intelligence (AI) has emerged as a promising tool for predicting cardiovascular events, offering the potential to enhance risk stratification and clinical decision-making. This review provides a comprehensive analysis of current trends and future directions in AI-based models for predicting cardiovascular events. We survey the literature to examine the various AI techniques, including machine learning (ML) algorithms, deep learning models, and ensemble methods, employed for cardiovascular risk prediction. Additionally, we discuss the key features, strengths, and limitations of these models, highlighting their potential clinical applications and challenges. Furthermore, we explore emerging trends such as multimodal data fusion, interpretability, and personalized medicine, and their implications for advancing cardiovascular risk prediction. By synthesizing existing research findings and identifying areas for future exploration, this review aims to provide insights for researchers, clinicians, and policymakers involved in cardiovascular disease management and prevention.