Abstract
Heart disease is one of the most prevalent and serious health widespread issues affecting elderly and middle-aged individuals globally. Cardiovascular diseases (CVDs) impose considerable morbidity and mortality rates and entail considerable financial strain on global healthcare infrastructures. According to the report of the World Health (WHO) Organization, the mortality rate of heart disease will increase to 23 million cases by 2030. In healthcare, predicting diseases and analyzing electronic health records to derive useful patterns aid in early and accurate CAD diagnosis. Hence, in this paper, we worked on the Z-Alizadeh Sani dataset to demonstrate the strong ability of the machine learning technique in predicting CAD. We also applied the genetic algorithm to reduce dimension by finding the important features of the neural network. The results showcased that our proposed method could diagnose CAD by achieving the highest accuracy, sensitivity, and AUC of 94.71%, 96.29%, and 93.5%, respectively.
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This research is part of the PID2022-137451OB-I00 project funded by the MCIN/AEI/10.13039/501100011033 and by FSE+.
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Hashemi, M. et al. (2024). Enhancing Coronary Artery Disease Classification Using Optimized MLP Based on Genetic Algorithm. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024. Lecture Notes in Computer Science, vol 14674. Springer, Cham. https://doi.org/10.1007/978-3-031-61140-7_11
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DOI: https://doi.org/10.1007/978-3-031-61140-7_11
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