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One of the main causes of death worldwide is cardiovascular disease (CVD). Effective treatment of this global concern depends on early detection, as well as management. Currently, people with established heart issues are treated by physicians and other medical experts, with detection at later stages of CVD. However, the burden of cardiovascular disease treatment can be significantly reduced if it was possible to accurately estimate a patient's CVD risk at the initial stages. Machine learning techniques have emerged as a viable method for improving CVD risk prediction, which enables treatments to be more effectively tailored to each individual patient's needs. An in-depth analysis of current research on machine learning applications for CVD risk prediction is provided in this publication. The paper discusses the benefits of applying machine learning techniques, various prediction algorithms, performance assessment, and current research limits. Our findings suggest that machine learning methods are useful for predicting CVD risk and have the potential to improve clinical judgment, which may help to lessen the burden of cardiovascular disease in the future. This research also helps to shape the medical field by providing insights on treating similar deadly diseases using AI and machine learning models.
By: Abhaya Saridena