Machine Learning-Driven Cardiovascular Risk Assessment: Synthesizing Contemporary Methods, Barriers, and Emerging Horizons

This review explores the application of machine learning (ML) models in cardiovascular disease (CVD) prediction, emphasizing their potential to revolutionize risk assessment and healthcare delivery. By examining traditional models such as Logistic Regression, Support Vector Machines, and Random Forest, alongside advanced approaches like XGBoost, the study highlights their strengths, limitations, and performance in various healthcare contexts. The review underscores the growing role of hybrid and explainable ML architectures, as well as the integration of deep learning techniques, in enhancing accuracy, scalability, and clinical trust. Despite notable advancements, challenges such as data quality, imbalance, and ethical considerations in underrepresented regions persist, underscoring the need for collaborative efforts among stakeholders to ensure equitable and efficient implementation. By addressing these gaps and leveraging robust models like XGBoost, ML has the potential to significantly reduce the global burden of CVDs and drive transformative change in precision medicine and patient-centred care.