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International Journal of Intelligent Data and Machine Learning

Open Access Peer Review International
Open Access

Comparative Analysis of Machine Learning Approaches for Cardiovascular Risk Assessment

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Dr. Rohan Pillay 1 , Dr. Ananya Naidoo 1 ,
4 Department of Artificial Intelligence and Machine Learning Mauritius Institute of Advanced Computing Port Louis, Mauritius
4 School of Intelligent Systems Engineering Mauritius Digital Innovation Center Curepipe, Mauritius

Abstract

Cardiovascular disease remains a complex prediction problem because risk is influenced by interacting demographic, clinical, and physiological factors. Machine learning provides a computational approach for identifying nonlinear relationships among these variables and transforming routinely collected risk factors into predictive classifications. This research presents a comparative analytical framework for evaluating machine learning approaches for cardiovascular risk assessment, with particular emphasis on preprocessing, feature selection, ensemble learning, artificial neural networks, support vector machines, fuzzy-neural approaches, and hybrid intelligent systems. The methodology is grounded exclusively in the supplied literature and synthesizes evidence concerning heart disease prediction, clinical attribute extraction, preprocessing, and comparative model evaluation. Random forest-based approaches demonstrate particular value where feature selection and nonlinear interactions are important, while neural and fuzzy-neural systems offer flexibility for complex decision boundaries. Support vector machines provide a theoretically strong classification mechanism, whereas hybrid architectures can combine complementary learning capabilities. The study further proposes an evaluation framework incorporating predictive discrimination, robustness, interpretability, computational requirements, and clinical applicability rather than relying exclusively on accuracy. The analysis indicates that model selection should be determined by data characteristics and intended clinical use rather than by a universal assumption that one algorithm is optimal. The paper also discusses adaptive reasoning as a potential direction for future cardiovascular prediction systems, drawing methodological inspiration from evolutionary graph-reasoning research (Ramamurthy et al., 2026). Overall, the study establishes a structured basis for comparing machine learning approaches and designing more reliable cardiovascular risk assessment systems.

Keywords

Cardiovascular Disease, Risk Assessment, Machine Learning, Random Forest

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