Performance improvement of decision trees for diagnosis of coronary artery disease using multi filtering approach

Abdar, Moloud and Nasarian, Elham and Zhou, Xujuan and Bargshady, Ghazal and Wijayaningrum, Vivi Nur and Hussain, Sadiq (2019) Performance improvement of decision trees for diagnosis of coronary artery disease using multi filtering approach. In: 4th IEEE International Conference on Computer and Communication Systems (ICCCS 2019), 23-25 Feb 2019 , Singapore .

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Abstract

The heart is one of the strongest muscular organs in the human body. Every year, this disease can kill many people in the world. Coronary artery disease (CAD) is named as the most common type of heart disease. Four well-known decision trees (DTs) are applied on the Z-Alizadeh Sani CAD dataset, which consists of J48, BF tree, REP tree, and NB tree. A multi filtering approach, named MFA, was used to modify the weight of attributes to improve the performance of DTs in this study. The model was applied on three main coronary arteries including the Left Anterior Descending (LAD), Left Circumflex (LCX), and Right Coronary Artery (RCA). The obtained results show that data balancing has a valuable impact on the performance of DTs. The comparison results show that this study provides the best results applied on the Z-Alizadeh Sani dataset compared to previous studies. The proposed MFA could improve the performance of the classic DTs algorithms significantly, with the highest accuracies obtained by NB tree for LAD, LCX, and RCA are 94.90%, 92.97% and 93.43%, respectively.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Accepted version deposited in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
Date Deposited: 02 May 2019 02:09
Last Modified: 05 Jul 2019 05:18
Uncontrolled Keywords: heart disease; coronary artery disease; data mining; machine learning, classification
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
URI: http://eprints.usq.edu.au/id/eprint/36137

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