Enhancing Cardiovascular Risk Prediction Using Artificial Intelligence And Deep Learning
Keywords:
Cardiovascular diseases, Risk prediction, Artificial intelligence, Deep learning, Machine learning, Electronic health records, Medical imaging, Wearable devices, personalized medicine, Predictive analytics.Abstract
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, necessitating accurate and timely risk prediction models. Traditional risk assessment tools, such as the Framingham Heart Study and QRISK, rely on limited clinical parameters and often fail to capture the complexity of individual risk profiles. Recent advancements in artificial intelligence (AI) and deep learning (DL) have demonstrated significant potential in enhancing cardiovascular risk prediction by integrating diverse data sources, including electronic health records (EHRs), imaging data, and wearable device outputs. This paper reviews the current state of AI and DL applications in cardiovascular risk assessment, evaluates their performance compared to traditional models, and discusses the challenges and future directions for their clinical implementation.
