Performance improvement of decision trees for diagnosis of coronary artery disease using multi filtering approach
Paper
Paper/Presentation Title | Performance improvement of decision trees for diagnosis of coronary artery disease using multi filtering approach |
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Presentation Type | Paper |
Authors | Abdar, Moloud (Author), Nasarian, Elham (Author), Zhou, Xujuan (Author), Bargshady, Ghazal (Author), Wijayaningrum, Vivi Nur (Author) and Hussain, Sadiq (Author) |
Journal or Proceedings Title | Proceedings of the 4th IEEE International Conference on Computer and Communication Systems (ICCS 2019) |
Number of Pages | 5 |
Year | 2019 |
Place of Publication | Singapore |
ISBN | 9781728113210 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/CCOMS.2019.8821633 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/8821633 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/8811523/proceeding |
Conference/Event | 4th IEEE International Conference on Computer and Communication Systems (ICCCS 2019) |
Event Details | 4th IEEE International Conference on Computer and Communication Systems (ICCCS 2019) Parent IEEE International Conference on Computer and Communications Event Date 23 to end of 25 Feb 2019 Event Location Singapore |
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. |
Keywords | heart disease; coronary artery disease; data mining; machine learning, classification |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Institution of Origin | University of Southern Queensland |
Byline Affiliations | University of Montreal, Canada |
Islamic Azad University, Iran | |
School of Management and Enterprise | |
Brawijaya University, Indonesia | |
Dibrugarh University, India |
https://research.usq.edu.au/item/q52v8/performance-improvement-of-decision-trees-for-diagnosis-of-coronary-artery-disease-using-multi-filtering-approach
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