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Dr. C. Priya serves as Deputy Dean and Professor in the Department of Electronics and Communication Engineering at Siddharth Institute of Engineering & Technology, Puttur, Andhra Pradesh, India. Her responsibilities include academic leadership, teaching, and research. Her professional role combines engineering education with institutional administration. Her chapter contribution focuses on machine learning-based cardiovascular disease prediction and risk stratification.
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.