Open Access
ADVANCED MACHINE LEARNING FOR CARDIAC DISEASE CLASSIFICATION: A PERFORMANCE ANALYSIS
4
Department of Biomedical Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
4
Department of Computer Science, University of Ghana, Legon, Ghana
Abstract
Heart disease remains a leading cause of morbidity and mortality globally, necessitating accurate and early diagnostic tools to improve patient outcomes. The escalating volume of healthcare data, coupled with advancements in computational capabilities, has positioned machine learning (ML) as a transformative approach for enhancing the classification of cardiac conditions. This article provides a comprehensive evaluation of machine learning models, particularly focusing on Multilayer Perceptron (MLP) and Support Vector Machine (SVM) architectures, for their efficacy in classifying heart disease. We delve into the methodologies employed, including feature selection and model training, and analyze their performance metrics. The discussion highlights how these advanced computational techniques contribute to more precise, efficient, and reliable diagnostic support systems, thereby aiding clinicians in early detection and personalized treatment strategies.
Keywords
Machine learning,
cardiac disease classification,
cardiovascular diagnosis
References
Kuruvilla, A. M., & Balaji, N. (2021). Heart disease prediction system using Correlation Based Feature Selection with Multilayer Perceptron approach. IOP Conference Series: Materials Science and Engineering, 1085(1), 012028. https://doi.org/10.1088/1757-899x/1085/1/012028
Kaya, M. O. (2021). Performance Evaluation of Multilayer Perceptron Artificial Neural Network Model in the Classification of Heart Failure. The Journal of Cognitive Systems, 6(1), 35–38. https://doi.org/10.52876/jcs.913671
Zheng, J., Jiang, Y., & Yan, H. (2006). Committee machines with ensembles of multilayer perceptron for the support of diagnosis of heart diseases. 2006 International Conference on Communications, Circuits and Systems, ICCCAS, Proceedings, 3, 2046–2050. https://doi.org/10.1109/ICCCAS.2006.285080
Krishna, C. L., & Reddy, P. V. S. (2019). An Efficient Deep Neural Network Multilayer Perceptron Based Classifier in Healthcare System. 2019 Proceedings of the 3rd International Conference on Computing and Communications Technologies, ICCCT 2019, 1–6. https://doi.org/10.1109/ICCCT2.2019.8824913
Vadicherla, D., & Sonawane, S. (2013). Decision support system for heart disease based on sequential minimal optimization in support vector machine. International Journal of Engineering Sciences & Emerging Technologies, 4(2), 19–26.
Yan, H., Zheng, J., Jiang, Y., Peng, C., & Li, Q. (2003). Development of a decision support system for heart disease diagnosis using multilayer perceptron. Proceedings - IEEE International Symposium on Circuits and Systems, 5. https://doi.org/10.1109/iscas.2003.1206411
Karaduzovic-Hadziabdic, K., & Köker, R. (2015). Diagnosis of heart disease using a committee machine neural network. Proceedings of the 9th International Conference on Applied Informatics, May, 351–360. https://doi.org/10.14794/icai.9.2014.1.351
Masih, N., Naz, H., & Ahuja, S. (2021). Multilayer perceptron based deep neural network for early detection of coronary heart disease. Health and Technology, 11(1), 127–138. https://doi.org/10.1007/s12553-020-00509-3
Naraei, P., Abhari, A., & Sadeghian, A. (2017). Application of multilayer perceptron neural networks and support vector machines in classification of healthcare data. FTC 2016 - Proceedings of Future Technologies Conference, 848–852. https://doi.org/10.1109/FTC.2016.7821702
Tarle, B., & Jena, S. (2017, July 2). An Artificial Neural Network Based Pattern Classification Algorithm for Diagnosis of Heart Disease. 2017 International Conference on Computing, Communication, Control and Automation, ICCUBEA 2017. https://doi.org/10.1109/ICCUBEA.2017.8463729
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