Open Access

Comparative Analysis of Machine Learning Approaches for Cardiovascular Risk Assessment

PDF

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

References

A. Aslam and A. Hussain, “A performance analysis of machine learning techniques for credit card fraud detection,” J. Artif. Intell., vol. 6, pp. 1–21, 2024.

A. Hussain, A. Khatoon, A. Aslam, and M. A. Khosa, “A comparative performance analysis of machine learning models for intrusion detection classification,” J. Cyber Secur., vol. 6, no. 1, pp. 1–23, 2024.

A. U. Haq, J. P. Li, M. H. Memon, S. Nazir, and R. Sun, “A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms,” Mob. Inf. Syst., vol. 2018, no. 8, pp. 1–21, 2018.

A. Amato, A. López, E. M. Peña-Méndez, P. Va ˇnhara, A. Hampl and J. Havel, Artificial Neural Networks in Medical Diagnosis. Warsaw, Poland, Elsevier, vol. 11, pp. 47–58, 2013.

C. Trevisan, G. Sergi, and S. Maggi, “Gender differences in brain-heart connection,” Brain Heart Dynam., pp. 937–951, 2020.

D. C. Yadav and S. Pal, “Prediction of heart disease using feature selection and random forest ensemble method,” Int. J. Pharm. Res., vol. 12, no. 4, pp. 56–66, 2020.

F. Amato, A. López, E. M. Peña-Méndez, P. Va ˇnhara, A. Hampl and J. Havel, Artificial Neural Networks in Medical Diagnosis. Warsaw, Poland, Elsevier, vol. 11, pp. 47–58, 2013.

H. Benhar, A. Idri, and J. Fernández-Alemán, “Data preprocessing for heart disease classification: A systematic literature review,” Comput. Methods Programs Biomed., vol. 195, no. 5, pp. 105635, 2020.

J. Mourao-Miranda, A. L. Bokde, C. Born, H. Hampel, and M. Stetter, “Classifying brain states and determining the discriminating activation patterns: Support vector machine on functional MRI data,” NeuroImage, vol. 28, no. 4, pp. 980–995, 2005.

K. Uyar and A. ̇Ilhan, “Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks,” Procedia Comput. Sci., vol. 120, no. 3, pp. 588–593, 2017.

M. M. Alam et al., “D-care: A non-invasive glucose measuring technique for monitoring diabetes patients,” in Proc. Int. Joint Conf. Comput. Intell., Springer, 2020, pp. 443–453.

N. Kausar, S. Palaniappan, B. B. Samir, A. Abdullah, and N. Dey, “Systematic analysis of applied data mining based optimization algorithms in clinical attribute extraction and classification for diagnosis of cardiac patients,” Appl. Intell. Optimiz. Biology Med.: Current Trends Open Prob., vol. 96, pp. 217–231, 2016.

S. Ghwanmeh, A. Mohammad, and A. Al-Ibrahim, “Innovative artificial neural networks-based decision support system for heart diseases diagnosis,” J. Intell. Learn. Syst. Appl., vol. 5, no. 3, pp. 176–183, 2013.

S. Pouriyeh, S. Vahid, G. Sannino, G. de Pietro, H. Arabnia and J. Gutierrez, “A comprehensive investigation and comparison of machine learning techniques in the domain of heart disease,” in 2017 IEEE Symp. Comput. Commun. (ISCC), IEEE, 2017, pp. 204–207.

A. Saboor, M. Usman, S. Ali, A. Samad, M. F. Abrar and N. Ullah, “A method for improving prediction of human heart disease using machine learning algorithms,” Mob. Inf. Syst., vol. 2022, no. 15, pp. 1–9, 2022.

Ramamurthy, K., Gumber, S., Abdelfattah, W.M. et al. Human AI trust modeling in cognitive systems via ensemble learning and advanced feature engineering. Discov Artif Intell 6, 366 (2026). https://doi.org/10.1007/s44163-026-01255-7

Geo Philip, Paulson, Artificial Intelligence and Machine Learning Applications in Project Schedule Forecasting: A Predictive Framework for Time-Control in Complex Building Projects (April 16, 2026). Available at SSRN: https://ssrn.com/abstract=6588119 or http://dx.doi.org/10.2139/ssrn.6588119

Most read articles by the same author(s)

1 2 3 4 5 6 > >>

International Journal of Intelligent Data and Machine Learning

Comparative Analysis of Machine Learning Approaches for Cardiovascular Risk Assessment

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles