Utilizing Machine Learning for the Early Detection of Coronary Heart Disease

Authors

  • Mudhafar jalil Jassim Ghrabat Iraqi Commission for Computers and Informatics, The Informatics Institute for Postgraduate Studies, Baghdad 10013, Iraq | Design and IoT Lab, Al-Turath University, Baghdad, 10013, Iraq
  • Siamand Hassan Mohialdin Nursing College, Hawler Medical University, Erbil, Iraq
  • Luqman Qader Abdulrahman College of Health Science, Hawler Medical University, Erbil, Iraq
  • Murthad Hussein Al-Yoonus Department of Information Technology, Noble Technical Institute, Erbil, Iraq
  • Zaid Ameen Abduljabbar Department of Computer Science, College of Education for Pure Sciences, University of Basrah, Basrah, 61004, Iraq | Huazhong University of Science and Technology, Shenzhen Institute, Shenzhen, 518000, China
  • Dhafer G. Honi Department of Computer Science, College of Education for Pure Sciences, University of Basrah, Basrah, 61004, Iraq | Department of IT, University of Debrecen, Debrecen, 4002, Hungary
  • Vincent Omollo Nyangaresi Department of Computer Science and Software Engineering, Jaramogi Oginga Odinga University of Science & Technology, Bondo, 40601, Kenya | Department of Applied Electronics, Saveetha School of Engineering, SIMATS, Chennai, Tami lnadu, 602105, India
  • Iman Qayes Abduljaleel Department of Computer Science, College of Education for Pure Sciences, University of Basrah, Basrah, 61004, Iraq
  • Husam A. Neamah Department of Electrical Engineering and Mechatronics, Faculty of Engineering, University of Debrecen, Debrecen, 4028, Ótemető u.4-5, Hungary
Volume: 14 | Issue: 5 | Pages: 17363-17375 | October 2024 | https://doi.org/10.48084/etasr.8171

Abstract

Coronary Heart Disease (CHD) is a persistent health issue, and risk prognosis is very important because it creates opportunities for doctors to provide early solutions. Despite such promising results, this type of analysis runs into several problems, such as accurately handling high-dimensional data because of the abundance of extracted information that hampers the prediction process. This paper presents a new approach that integrates Principal Component Analysis (PCA) and feature selection techniques to improve the prediction performance of CHD models, especially in light of dimensionality consideration. Feature selection is identified as one of the contributors to enhance model performance. Reducing the input space and identifying important attributes related to heart disease offers a refined approach to CHD prediction. Then four classifiers were used, namely PCA, Random Forest (RF), Decision Trees (DT), and AdaBoost, and an accuracy of approximately 96% was achieved, which is quite satisfactory. The experimentations demonstrated the effectiveness of this approach, as the proposed model was more effective than the other traditional models including the RF and LR in aspects of precision, recall, and AUC values. This study proposes an approach to reduce data dimensionality and select important features, leading to improved CHD prediction and patient outcomes.

Keywords:

random forest, decision trees, Principal Component Analysis (PCA), Machine Learning (ML) classifiers, Coronary Heart Disease (CHD), hyperparameters, prediction

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How to Cite

[1]
Ghrabat, M. jalil J., Mohialdin, S.H., Abdulrahman, L.Q., Al-Yoonus, M.H., Abduljabbar, Z.A., Honi, D.G., Nyangaresi, V.O., Abduljaleel, I.Q. and Neamah, H.A. 2024. Utilizing Machine Learning for the Early Detection of Coronary Heart Disease. Engineering, Technology & Applied Science Research. 14, 5 (Oct. 2024), 17363–17375. DOI:https://doi.org/10.48084/etasr.8171.

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