Authors : Bashir Ssimbwa; Azizi Wasike; Jamir Ssebadduka

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/2katjsb8

Scribd : https://tinyurl.com/2sx8b2n2

DOI : https://doi.org/10.38124/ijisrt/25nov1523

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : 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.

Keywords : Data Science, Big Data, Machine Learning, Artificial Intelligence, Computer Science, Cardiovascular Disease Prediction, Machine Learning Models, Machine Learning Architectures.

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