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

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  • Javad Hassannataj Joloudari

    (Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand 97175/615, 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 97175/615, Iran)

  • Mohammad Ghasemigol

    (Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand 97175/615, Iran)

  • Seyyed Mohammad Razavi

    (Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand 9717434765, Iran)

  • Amir Mosavi

    (Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
    Institute of Structural Mechanics, Bauhaus Universität-Weimar, D-99423 Weimar, Germany
    Department of Mathematics and Informatics, J. Selye University, 94501 Komarno, Slovakia
    Faculty of Health, Queensland University of Technology, 130 Victoria Park Road, Queensland 4059, Australia)

  • Narjes Nabipour

    (Department Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam)

  • Shahaboddin Shamshirband

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

  • Laszlo Nadai

    (Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary)

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.

Suggested Citation

  • Javad Hassannataj Joloudari & Edris Hassannataj Joloudari & Hamid Saadatfar & Mohammad Ghasemigol & Seyyed Mohammad Razavi & Amir Mosavi & Narjes Nabipour & Shahaboddin Shamshirband & Laszlo Nadai, 2020. "Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model," IJERPH, MDPI, vol. 17(3), pages 1-24, January.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:3:p:731-:d:312312
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    References listed on IDEAS

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    1. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
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    Cited by:

    1. Haewon Byeon, 2020. "Is the Random Forest Algorithm Suitable for Predicting Parkinson’s Disease with Mild Cognitive Impairment out of Parkinson’s Disease with Normal Cognition?," IJERPH, MDPI, vol. 17(7), pages 1-14, April.
    2. Sadegh Fathi & Hassan Sajadzadeh & Faezeh Mohammadi Sheshkal & Farshid Aram & Gergo Pinter & Imre Felde & Amir Mosavi, 2020. "The Role of Urban Morphology Design on Enhancing Physical Activity and Public Health," IJERPH, MDPI, vol. 17(7), pages 1-29, March.
    3. Alaa M. Elsayad & Ahmed M. Nassef & Mujahed Al-Dhaifallah & Khaled A. Elsayad, 2020. "Classification of Biodegradable Substances Using Balanced Random Trees and Boosted C5.0 Decision Trees," IJERPH, MDPI, vol. 17(24), pages 1-20, December.

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