Cardiovascular diseases (CVDs) are one of two leading causes of mortality globally, resulting in millions of deaths each year. A major barrier to achieving improved results is the difficulty in detecting these conditions early enough. In response, many researchers and clinicians have adopted machine learning (ML) approaches as a way to support healthcare. Unlike traditional tools, ML can uncover hidden patterns in patient data that might otherwise go unnoticed. This review explores three main categories of machine learning approaches i.e. classical algorithms, deep learning, and hybrid systems. It also highlights commonly used models like Random Forest, Artificial Neural Networks, and boosting approaches that are gaining traction in advanced CVD prediction research. For these systems to work effectively, careful attention must be given to the data used; how it’s processed, which features are selected, and how model performance is measured. While the preliminary results are promising and show real potential in assisting clinicians with diagnosis and early intervention, some challenges persist. Issues such as lack of transparency, uneven data, and privacy concerns keep restricting large-scale ML integration. Nevertheless, with continued refinement and clinical validation, ML could become an important part of the future of cardiovascular prediction and prevention.
Mahnoor Kashif (Thu,) studied this question.