Volume 11, Issue 1 (6-2024)
jhbmi 2024, 11(1): 26-42 |
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Motamed S, Askari E. Diagnosing Heart Disease Using Convolutional Neural Network and the Particle Swarm Optimization. jhbmi 2024; 11 (1) :26-42
URL: http://jhbmi.ir/article-1-840-en.html
URL: http://jhbmi.ir/article-1-840-en.html
Assistant Professor, Department of Computer Engineering, Fouman and Shaft Branch, Islamic Azad University, Fouman, Iran
Abstract: (987 Views)
Introduction: The human heart is a physiologically vital organ whose signals can be continuously recorded using an electrocardiogram (ECG) device. Cardiovascular diseases are one of the leading causes of mortality worldwide. Timely and accurate identification of this condition, along with preventive measures, can help reduce the incidence of cardiovascular diseases.
Method: This article aimed to predict a group of heart failures based on the patterns found in extracted features from patients with cardiac arrhythmias, distinguishing them from samples in a normal state. The proposed model involves preprocessing operations, such as discretization and replacement of missing values using column-wise averaging, on the dataset. Subsequently, feature selection operations were performed on normalized data to reduce complexity and improve speed and accuracy. The data is then fed into decision tree classifiers, k-nearest neighbors, naive Bayes, and convolutional neural networks.
Results: A comparison of the accuracy obtained from different algorithms before and after applying the proposed method reveals improved performance across all methods after implementation. Particularly, the convolutional neural network demonstrates superior performance.
Conclusion: Based on the results, it can be concluded that the proposed model achieves an accuracy of 92.34%, surpassing other methods.
Method: This article aimed to predict a group of heart failures based on the patterns found in extracted features from patients with cardiac arrhythmias, distinguishing them from samples in a normal state. The proposed model involves preprocessing operations, such as discretization and replacement of missing values using column-wise averaging, on the dataset. Subsequently, feature selection operations were performed on normalized data to reduce complexity and improve speed and accuracy. The data is then fed into decision tree classifiers, k-nearest neighbors, naive Bayes, and convolutional neural networks.
Results: A comparison of the accuracy obtained from different algorithms before and after applying the proposed method reveals improved performance across all methods after implementation. Particularly, the convolutional neural network demonstrates superior performance.
Conclusion: Based on the results, it can be concluded that the proposed model achieves an accuracy of 92.34%, surpassing other methods.
Keywords: Cardiac Arrhythmia, Convolutional Neural Network, Particle Swarm Optimization, Multiclass Classification
Type of Study: Original Article |
Subject:
Artificial Intelligence in Healthcare
Received: 2024/01/9 | Accepted: 2024/05/29
Received: 2024/01/9 | Accepted: 2024/05/29
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