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Copyright © 2022 Jamshid Pirgazi et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

In recent years, Internet of Medical Things (IoMT) and machine learning (ML) have played a major role in the healthcare industry and prediction of in time diagnosis of diseases. Heart disease has long been considered one of the most common and lethal causes of death. Accordingly, in this paper, a multiple-step method using IoMT and ML has been proposed for diagnosis of heart disease based on image and numerical resources. In the first step, transfer learning based on convolutional neural network (CNN) is used for feature extraction. In the second step, three methods of distributed stochastic neighbor embedding (t-SNE), F-score, and correlation-based feature selection (CFS) are utilized to select the best features. In the end, a combination of outputs of three classifiers including Gaussian Bayes (GB), support vector machine (SVM), and random forest (RF) according to the majority voting is employed for diagnosis of the conditions of heart disease patients. The results were evaluated on the two UCI datasets. The results indicate the improvement of performance compared to other methods.

Details

Title
An Accurate Heart Disease Prognosis Using Machine Intelligence and IoMT
Author
Pirgazi, Jamshid 1   VIAFID ORCID Logo  ; Ali Ghanbari Sorkhi 1   VIAFID ORCID Logo  ; Majid Iranpour Mobarkeh 2   VIAFID ORCID Logo 

 Department of Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran 
 Department of Computer Engineering and IT, Payam Noor University, Tehran, Iran 
Editor
Mohammad R Khosravi
Publication year
2022
Publication date
2022
Publisher
John Wiley & Sons, Inc.
e-ISSN
15308677
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2687529936
Copyright
Copyright © 2022 Jamshid Pirgazi et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.