Cardiovascular disease (CVD) remains a leading cause of mortality worldwide, and the prevention and management of this disease represent significant challenges for healthcare providers. Machine learning has emerged as a promising tool to improve the prevention and management of CVD. In this comprehensive review, we provide an overview of the role of machine learning in CVD prevention, including its use in predictive analytics, early detection, personalized medicine, telemedicine, and real-time decision support. We also discuss the challenges and future directions for machine learning in CVD prevention and the implications for clinical practice. The integration of machine learning in CVD prevention has the potential to improve patient outcomes and reduce the burden of this disease on society, and this review provides a comprehensive analysis of the current state of the field. The potential of machine learning to transform CVD prevention is substantial, but its implementation also poses important challenges that need to be addressed. As more research and development continue in this field, it is important to continue to evaluate the efficacy of machine learning in clinical settings and ensure that its use is ethical and equitable. Ultimately, the successful implementation of machine learning in CVD prevention will require collaboration between clinicians, researchers, data scientists, and policymakers.

Cardiovascular diseases; Machine Learning; Data Science; Clinical Application