General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Mia Hu
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    • Average Days from Submission to Acceptance: 192 days
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Editor-in-chief
Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2023 Vol.15(3): 95-100
DOI: 10.7763/IJCTE.2023.V15.1337

A Deep-Learning Neural Network-Based Predictive System for the Occurrence of Major Adverse Cardiovascular Events (MACE) in Patients with Acute Myocardial Infarction

Syed Waseem Abbas Sherazi, Huilin Zheng, Saba Arif, Malik Muhammad Waqar, Gyeongtae Kim, and Jong Yun Lee*

Manuscript received December 16, 2022; revised Jauary 18, 2023; accepted February 15, 2023.

Abstract—Deep-learning is an emerging technology in health informatics nowadays. Therefore, this paper proposes a novel Deep Neural Network (DNN)-based diagnosis system for Cardiovascular Disease (CVD) in patients with Acute Myocardial Infarction (AMI). In this research, Korea Acute Myocardial Infarction Registry (KAMIR-IV) dataset is used and 11,189 subjects are extracted after data pre-processing, and then divided into two subdatasets such as males’ and females’ datasets. Later, all datasets are splitted into training and test datasets, and consequently, the Synthetic Minority Oversampling Technique (SMOTE) on training data for data imbalance problem has been applied. The proposed prediction model is trained on oversampled training data, and hyperparameters are tuned using grid search approach. Following, the performance of proposed model is evaluated using performance measures such as accuracy, precision, recall, F1-score, and the Area under the ROC Curve (AUC). The proposed DNN-based prediction model achieved an accuracy of 0.9835, a precision of 0.9835, a recall of 0.9835, an F1-score of 0.9834, and an AUC of 0.9943 on a complete dataset whereas, the accuracy of 0.9713, a precision of 0.9710, a recall of 0.9713, an F1-score of 0.9710, and an AUC of 0.9989 on males’ subdata and an accuracy of 0.9607, a precision of 0.9701, a recall of 0.9613, an F1-score of 0.9720, and an AUC of 0.9985 on females’ subdata. In addition, a web-based decision support system is developed and deployed on the local server for physicians, doctors, and CVD patients. Consequently, our finding was that the proposed diagnosis system is predicting efficiently for all patients and diagnosing the major adverse cardiovascular events’ (MACE) occurrences accurately in order to select the proper treatment for patients with AMI.

Index Terms—Cardiovascular disease, deep neural network, machine learning, diagnosis system, Major adverse Cardiovascular Events (MACE), Acute Myocardial Infarction (AMI)

Syed Waseem Abbas Sherazi, Huilin Zheng, Saba Arif, Malik Muhammad Waqar, Gyeongtae Kim, and Jong Yun Lee are with Department of Computer Science, Chungbuk National University, Cheongju, Chungbuk 28644, South Korea.
E-mail: waseemsherazi512@gmail.com (S.W.A.S.), huilin@chungbuk.ac.kr (H.Z.), sababukhari0346@gmail.com (S.A.), malikwaqarhaider@gmail.com (M.M.W.), adfsfsf@naver.com (G.K.)
*Correspondence: jongyun@chungbuk.ac.kr (J.Y.L)

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Cite:Syed Waseem Abbas Sherazi, Huilin Zheng, Saba Arif, Malik Muhammad Waqar, Gyeongtae Kim, and Jong Yun Lee, "A Deep-Learning Neural Network-Based Predictive System for the Occurrence of Major Adverse Cardiovascular Events (MACE) in Patients with Acute Myocardial Infarction," International Journal of Computer Theory and Engineering vol. 15, no. 3, pp. 95-100, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


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