Authors : Lawal Olayinka Olusegun; Alowolodu Olufunso Dayo; Obe Olumide Olayinka; Adetunmbi Adebayo Olusola

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/42z5vb6s

Scribd : https://tinyurl.com/jb8n678a

DOI : https://doi.org/10.38124/ijisrt/26apr1555

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

Abstract : Cardiovascular diseases (CVDs) contribute significantly to global health challenges, demanding prompt early detection and intervention to reduce harmful outcomes. The application of machine learning (ML) techniques offers a viable avenue to revolutionise conventional diagnostic methods in CVD detection. This review examined the ML algorithms for risk prediction and diagnosis of various CVDs, including arrhythmias, heart failure (HF), and coronary artery disease (CAD), emphasising diverse approaches, challenges, and avenues for future research. Machine learning models utilise complex patterns found within extensive Clinical datasets encompassing electronic health records (EHRs) and diagnostic imaging to improve early diagnosis and individualised treatment management strategies for affected individuals at risk for CVD diseases. This study defines open research problems requiring further investigation to enhance the efficacy and clinical applicability of ML models in combating cardiovascular diseases. This work recommends reliable and interpretable ML models, integration of heterogeneous data sources, collaborative efforts to address data scarcity, and advancements in model transparency and explainability. The integration of ML techniques holds great promise for advancing CVD detection and improving patient outcomes. Overcoming the challenges outlined in this review and examining opportunities for future study can unlock the full potential of ML in alleviating the global burden of CVD-related morbidity and mortality.

Keywords : Machine Learning, Cardiovascular Disease Detection, Risk Prediction, Diagnosis, Challenges, Open Research Problems.

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