| Autor: |
Joloudari JH; Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran., Saadatfar H; Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran., GhasemiGol M; Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran., Alizadehsani R; Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, Australia., Sani ZA; Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.; Omid hospital, Iran University of Medical Sciences, Tehran, Iran., Hasanzadeh F; Omid hospital, Iran University of Medical Sciences, Tehran, Iran., Hassannataj E; Department of Nursing, School of Nursing and Allied Medical Sciences, Maragheh Faculty of Medical Sciences, Maragheh, Iran., Sharifrazi D; Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran., Mansor Z; Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Malaysia. |
| Abstrakt: |
Cardiovascular disease is one of the most challenging diseases in middle-aged and older people, which causes high mortality. Coronary artery disease (CAD) is known as a common cardiovascular disease. A standard clinical tool for diagnosing CAD is angiography. The main challenges are dangerous side effects and high angiography costs. Today, the development of artificial intelligence-based methods is a valuable achievement for diagnosing disease. Hence, in this paper, artificial intelligence methods such as neural network (NN), deep neural network (DNN), and fuzzy C-means clustering combined with deep neural network (FCM-DNN) are developed for diagnosing CAD on a cardiac magnetic resonance imaging (CMRI) dataset. The original dataset is used in two different approaches. First, the labeled dataset is applied to the NN and DNN to create the NN and DNN models. Second, the labels are removed, and the unlabeled dataset is clustered via the FCM method, and then, the clustered dataset is fed to the DNN to create the FCM-DNN model. By utilizing the second clustering and modeling, the training process is improved, and consequently, the accuracy is increased. As a result, the proposed FCM-DNN model achieves the best performance with a 99.91% accuracy specifying 10 clusters, i.e., 5 clusters for healthy subjects and 5 clusters for sick subjects, through the 10-fold cross-validation technique compared to the NN and DNN models reaching the accuracies of 92.18% and 99.63%, respectively. To the best of our knowledge, no study has been conducted for CAD diagnosis on the CMRI dataset using artificial intelligence methods. The results confirm that the proposed FCM-DNN model can be helpful for scientific and research centers. |
