International Journal of Cardiovascular Research & Innovation

ABSTRACT

Cardiovascular diseases (CVDs) remain a leading cause of morbidity and mortality worldwide. Early identification and timely intervention are crucial for reducing the burden of these diseases. Traditional risk assessment methods, while valuable, often rely on a limited set of risk factors and may not accurately predict individual risk. Artificial intelligence (AI) has emerged as a powerful tool with the potential to revolutionize cardiovascular disease prediction and management. AI-driven algorithms can analyze complex patterns within large datasets, integrating a wide range of clinical, demographic, and lifestyle factors to identify individuals at high risk of cardiovascular events. This proactive approach enables early intervention strategies to mitigate risk and improve patient outcomes. This review delves into the application of AI algorithms in cardiovascular disease prediction, exploring their strengths, limitations, and comparative performance. We discuss various AI techniques, including traditional machine learning algorithms and deep learning architectures. Furthermore, we examine the challenges and opportunities associated with integrating AI-driven prediction models into clinical practice, including data quality, model interpretability, and ethical considerations. By addressing these challenges and leveraging the potential of AI, we can develop more accurate, personalized, and timely prediction models to improve cardiovascular health and reduce the global burden of CVDs.

Article History

KEYWORDS

1. Cardiovascular disease prediction
2. AI-driven algorithms
3. Machine learning
4. AI-driven algorithms
5. Risk assessment