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

Moloud Abdar; Elham Nasarian; Xujuan Zhou; Ghazal Bargshady; Vivi Nur Wijayaningrum; Sadiq Hussain

doi:10.1109/CCOMS.2019.8821633

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

Moloud Abdar
, Elham Nasarian
, Xujuan Zhou
, Ghazal Bargshady
, Vivi Nur Wijayaningrum
, Sadiq Hussain

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

33 Citations (Scopus)

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.

Original language	English
Title of host publication	2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019
Editors	Yang Xiao, Nobuo Funabiki
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	26-30
Number of pages	5
ISBN (Electronic)	9781728113227
ISBN (Print)	9781728113234
DOIs	https://doi.org/10.1109/CCOMS.2019.8821633
Publication status	Published - Feb 2019
Externally published	Yes
Event	4th IEEE International Conference on Computer and Communication Systems, ICCCS 2019 - Singapore, Singapore Duration: 23 Feb 2019 → 25 Feb 2019

Publication series

Name	2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019

Conference

Conference	4th IEEE International Conference on Computer and Communication Systems, ICCCS 2019
Country/Territory	Singapore
City	Singapore
Period	23/02/19 → 25/02/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1109/CCOMS.2019.8821633

Cite this

Abdar, M., Nasarian, E., Zhou, X., Bargshady, G., Wijayaningrum, V. N., & Hussain, S. (2019). Performance improvement of decision trees for diagnosis of coronary artery disease using multi filtering approach. In Y. Xiao, & N. Funabiki (Eds.), 2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019 (pp. 26-30). Article 8821633 (2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CCOMS.2019.8821633

Abdar, Moloud ; Nasarian, Elham ; Zhou, Xujuan et al. / Performance improvement of decision trees for diagnosis of coronary artery disease using multi filtering approach. 2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019. editor / Yang Xiao ; Nobuo Funabiki. United States : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 26-30 (2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019).

@inproceedings{a90b165c16d14997b75871b65067b7ef,

title = "Performance improvement of decision trees for diagnosis of coronary artery disease using multi filtering approach",

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 = "Classification, Coronary artery disease, Data mining, Heart disease, Machine learning",

author = "Moloud Abdar and Elham Nasarian and Xujuan Zhou and Ghazal Bargshady and Wijayaningrum, \{Vivi Nur\} and Sadiq Hussain",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 4th IEEE International Conference on Computer and Communication Systems, ICCCS 2019 ; Conference date: 23-02-2019 Through 25-02-2019",

year = "2019",

month = feb,

doi = "10.1109/CCOMS.2019.8821633",

language = "English",

isbn = "9781728113234",

series = "2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "26--30",

editor = "Yang Xiao and Nobuo Funabiki",

booktitle = "2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019",

address = "United States",

}

Abdar, M, Nasarian, E, Zhou, X, Bargshady, G, Wijayaningrum, VN & Hussain, S 2019, Performance improvement of decision trees for diagnosis of coronary artery disease using multi filtering approach. in Y Xiao & N Funabiki (eds), 2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019., 8821633, 2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 26-30, 4th IEEE International Conference on Computer and Communication Systems, ICCCS 2019, Singapore, Singapore, 23/02/19. https://doi.org/10.1109/CCOMS.2019.8821633

Performance improvement of decision trees for diagnosis of coronary artery disease using multi filtering approach. / Abdar, Moloud; Nasarian, Elham; Zhou, Xujuan et al.
2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019. ed. / Yang Xiao; Nobuo Funabiki. United States: IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 26-30 8821633 (2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Performance improvement of decision trees for diagnosis of coronary artery disease using multi filtering approach

AU - Abdar, Moloud

AU - Nasarian, Elham

AU - Zhou, Xujuan

AU - Bargshady, Ghazal

AU - Wijayaningrum, Vivi Nur

AU - Hussain, Sadiq

PY - 2019/2

Y1 - 2019/2

N2 - 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.

AB - 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.

KW - Classification

KW - Coronary artery disease

KW - Data mining

KW - Heart disease

KW - Machine learning

UR - https://www.scopus.com/pages/publications/85072981731

UR - https://iccc2019.ieee-iccc.org/

U2 - 10.1109/CCOMS.2019.8821633

DO - 10.1109/CCOMS.2019.8821633

M3 - Conference contribution

AN - SCOPUS:85072981731

SN - 9781728113234

T3 - 2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019

SP - 26

EP - 30

BT - 2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019

A2 - Xiao, Yang

A2 - Funabiki, Nobuo

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - United States

T2 - 4th IEEE International Conference on Computer and Communication Systems, ICCCS 2019

Y2 - 23 February 2019 through 25 February 2019

ER -

Abdar M, Nasarian E, Zhou X, Bargshady G, Wijayaningrum VN, Hussain S. Performance improvement of decision trees for diagnosis of coronary artery disease using multi filtering approach. In Xiao Y, Funabiki N, editors, 2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019. United States: IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 26-30. 8821633. (2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019). doi: 10.1109/CCOMS.2019.8821633

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

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