Proceedings of the 7th FIRST 2023 International Conference on Global Innovations (FIRST-ESCSI 2023)

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The Classification of Coronary Artery Disease Using A Machine Learning Approach: A Preliminary Study

Authors
Ahyar Supani1, 2, *, Siti Nurmaini3, Radiyati Umi Partan4, Bhakti Yudo Suprapto5
1Doctoral Program of Engineering Science, Universitas Sriwijaya, Palembang, Indonesia
2Polytechnic State of Sriwijaya, Palembang, Indonesia
3Intelligent System Research Group, Universitas Sriwijaya, Palembang, Indonesia
4Interrnal Medicine Department, Medicine Faculty, Universitas Sriwijaya, Palembang, Indonesia
5Electrical Engineering Department, Universitas Srwijaya, Palembang, Indonesia
*Corresponding author. Email: ahyarsupani@polsri.ac.id
Corresponding Author
Ahyar Supani
Available Online 27 February 2024.
DOI
10.2991/978-94-6463-386-3_2How to use a DOI?
Keywords
Xgboost; KNN; Machine Learning; Coronary Heart Disease
Abstract

Coronary heart disease (CAD) is the world’s leading cause of death. Early detection of this condition is critical. Diagnosis with visual images through examination with angiography techniques is the current gold standard. However, this technique causes side effects, so it is necessary to carry out alternative examinations based on symptoms that do not pose a risk. To increase the accuracy of CAD diagnosis based on symptoms, it can use computer assistance via machine learning. The aim of this research is to classify CAD or normal patients using a machine learning model approach using the Naïve Bayes, XGboost, and K-Nearest Neighbor (KNN) algorithms. The dataset used in the experiment is Z-Alizadeh Sani, which contains 55 features. The experimental results were evaluated using the metrics accuracy, IoU, precision, recall, and F1_score. Of the three algorithms tested, XGBoost produced the four highest scores regarding the metrics accuracy (0.852), IoU (0.824), recall (0.977), and F1_score (0.903), which outperformed the other two algorithms. KNN had the highest metric precision, with a value of 0.870. Overall, XGBoost remains superior in performance.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 7th FIRST 2023 International Conference on Global Innovations (FIRST-ESCSI 2023)
Series
Advances in Engineering Research
Publication Date
27 February 2024
ISBN
10.2991/978-94-6463-386-3_2
ISSN
2352-5401
DOI
10.2991/978-94-6463-386-3_2How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ahyar Supani
AU  - Siti Nurmaini
AU  - Radiyati Umi Partan
AU  - Bhakti Yudo Suprapto
PY  - 2024
DA  - 2024/02/27
TI  - The Classification of Coronary Artery Disease Using A Machine Learning Approach: A Preliminary Study
BT  - Proceedings of the 7th FIRST 2023 International Conference on Global Innovations (FIRST-ESCSI 2023)
PB  - Atlantis Press
SP  - 4
EP  - 12
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-386-3_2
DO  - 10.2991/978-94-6463-386-3_2
ID  - Supani2024
ER  -