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Association Between Work-Related Features and Coronary Artery Disease: A Heterogeneous Hybrid Feature Selection Integrated With Balancing Approach Publisher



Nasarian E1 ; Abdar M2 ; Fahami MA3 ; Alizadehsani R2 ; Hussain S4 ; Basiri ME5 ; Zomorodimoghadam M6 ; Zhou X7 ; Plawiak P8, 9 ; Acharya UR10, 11, 12 ; Tan RS13 ; Sarrafzadegan N14, 15
Authors
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Authors Affiliations
  1. 1. Department of Industrial Engineering, Islamic Azad University, Najafabad Branch, Najafabad, Iran
  2. 2. Institute for Intelligent Systems Research and Innovation (IISRI)Locked Bag 20000, Geelong, 3220, VIC, Australia
  3. 3. Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 841583111, Iran
  4. 4. System Administrator, Dibrugarh University, Dibrugarh, 786004, India
  5. 5. Department of Computer Engineering, Shahrekord University, Shahrekord, 64165478, Iran
  6. 6. Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, 9177948974, Iran
  7. 7. School of Management & Enterprise, University of Southern Queensland, QLD, 4300, Australia
  8. 8. Department of Information and Communications Technology, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24 st., F-3, Krakow, 31-155, Poland
  9. 9. Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Baltycka 5, Gliwice, 44-100, Poland
  10. 10. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore
  11. 11. Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
  12. 12. Department of Biomedical Engineerine, School of Science and Technology, Singapore University of Social Sciences, Singapore
  13. 13. Department of Cardiology, National Heart Centre Singapore, Singapore, 169609, Singapore
  14. 14. Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran
  15. 15. Faculty of Medicine, School of Population and Public Health, The University of British Columbia, Vancouver, Canada

Source: Pattern Recognition Letters Published:2020


Abstract

Coronary artery disease (CAD) is a leading cause of death worldwide and is associated with high healthcare expenditure. Researchers are motivated to apply machine learning (ML) for quick and accurate detection of CAD. The performance of the automated systems depends on the quality of features used. Clinical CAD datasets contain different features with varying degrees of association with CAD. To extract such features, we developed a novel hybrid feature selection algorithm called heterogeneous hybrid feature selection (2HFS). In this work, we used Nasarian CAD dataset, in which work place and environmental features are also considered, in addition to other clinical features. Synthetic minority over-sampling technique (SMOTE) and Adaptive synthetic (ADASYN) are used to handle the imbalance in the dataset. Decision tree (DT), Gaussian Naive Bayes (GNB), Random Forest (RF), and XGBoost classifiers are used. 2HFS-selected features are then input into these classifier algorithms. Our results show that, the proposed feature selection method has yielded the classification accuracy of 81.23% with SMOTE and XGBoost classifier. We have also tested our approach with other well-known CAD datasets: Hungarian dataset, Long-beach-va dataset, and Z-Alizadeh Sani dataset. We have obtained 83.94%, 81.58% and 92.58% for Hungarian dataset, Long-beach-va dataset, and Z-Alizadeh Sani dataset, respectively. Hence, our experimental results confirm the effectiveness of our proposed feature selection algorithm as compared to the existing state-of-the-art techniques which yielded outstanding results for the development of automated CAD systems. © 2020
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