Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model.

Journal: International journal of environmental research and public health
Published Date: Jan 23, 2020

Abstract

Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.

Authors

  • Javad Hassannataj Joloudari
    Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
  • Edris Hassannataj Joloudari
    Department of Nursing, School of Nursing and Allied Medical Sciences, Maragheh Faculty of Medical Sciences, Maragheh, Iran.
  • Hamid Saadatfar
    Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
  • Mohammad GhasemiGol
    Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
  • Seyyed Mohammad Razavi
    Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
  • Amir Mosavi
    Faculty of Informatics, Technische Universität Dresden, Dresden, Germany.
  • Narjes Nabipour
    Department Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.
  • Shahaboddin Shamshirband
    Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia; Department of Computer Science, Chalous Branch, Islamic Azad University (IAU), 46615-397 Chalous, Mazandaran, Iran. Electronic address: shamshirband@um.edu.my.
  • Laszlo Nadai
    Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary.

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