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Dr. Mohammed Faizal is a Senior Resident in the Department of Cardiology at Mohammed Bin Khalifa Al Salman Al Khalifa Cardiac Centre, Awali, Kingdom of Bahrain. His professional responsibilities involve clinical work in cardiology. His healthcare role is associated with cardiovascular assessment and patient care. His contribution to this chapter brings a clinical perspective to cardiovascular disease prediction and risk evaluation.
Cardiovascular diseases (CVDs) continue to be a leading cause of illness and death globally, highlighting the urgent requirement for early identification, precise risk assessment, and tailored preventative measures. ML has surfaced as a viable method for discerning intricate connections among demographic traits, clinical data, lifestyle variables, laboratory findings, medical backgrounds, and physiological indicators. This chapter explores the underpinnings of machine learning-driven cardiovascular disease forecasting, pertinent data repositories, preprocessing techniques, feature selection, model formulation, assessment metrics, interpretable artificial intelligence, and clinical assimilation. It offers a suggested methodology for stratifying cardiovascular risk and addresses the difficulties related to class imbalance, absent data, model generalisability, interpretability, equity, privacy, and clinical validation. The chapter wraps up by outlining research avenues related to multimodal learning, continuous patient monitoring, wearable technology, federated learning, and individualised risk assessment. Well-validated machine learning systems can enhance clinical decision-making and preventative care; nonetheless, they ought to supplement not supplant expert judgement and recognised clinical protocols.