Heart diseases are one of the important factors that threaten the health of the society. Accurate and early recognition of these diseases improves the possibility of optimal intervention and treatment and can improve the ability to increase survival and reduce disability. In recent years, with the advancement of technology and the development process of electronic medical systems, the use of ECG signals as a non-destructive and non-invasive method to diagnose heart diseases has increased. In this article, the combination of convolutional neural network (CNN) and fuzzy logic is used to automatically detect heart disease from ECG signals. The purpose of combining the CNN and fuzzy logic is that the system can deal with cognitive uncertainties more like humans and have the possibility of processing uncertain and incorrect information. The proposed model was tested on the MIT-BIH arrhythmia dataset and the results show that the proposed model, with 97.54% accuracy, shows better performance than other methods.
Motamed, S. and Askari, A. (2025). Prediction of Cardiovascular Diseases Using Convolutional Neural Network and Fuzzy Logic. Soft Computing Journal, (), -. doi: 10.22052/scj.2026.254434.1220
MLA
Motamed, S. , and Askari, A. . "Prediction of Cardiovascular Diseases Using Convolutional Neural Network and Fuzzy Logic", Soft Computing Journal, , , 2025, -. doi: 10.22052/scj.2026.254434.1220
HARVARD
Motamed, S., Askari, A. (2025). 'Prediction of Cardiovascular Diseases Using Convolutional Neural Network and Fuzzy Logic', Soft Computing Journal, (), pp. -. doi: 10.22052/scj.2026.254434.1220
CHICAGO
S. Motamed and A. Askari, "Prediction of Cardiovascular Diseases Using Convolutional Neural Network and Fuzzy Logic," Soft Computing Journal, (2025): -, doi: 10.22052/scj.2026.254434.1220
VANCOUVER
Motamed, S., Askari, A. Prediction of Cardiovascular Diseases Using Convolutional Neural Network and Fuzzy Logic. Soft Computing Journal, 2025; (): -. doi: 10.22052/scj.2026.254434.1220
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