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Model uncertainty quantification for diagnosis of each main coronary artery stenosis

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Abstract

One of the main causes of death in the world is coronary artery disease (CAD). CAD occurs when there is stenosis in one or more of the three major coronary arteries: right coronary artery (RCA), left circumflex (LCX) artery, and left anterior descending (LAD) artery. The gold standard or CAD diagnosis is angiography, but it is invasive, costly, and time consuming. Therefore, researchers continually seek new machine learning methods that can screen for CAD non-invasively. For reliable and cost-effective CAD diagnosis, several algorithms have been developed. Most prior studies analyzed the presence or absence of CAD in a dichotomous manner. Herein, we studied the more complex problem of classification of stenosis in individual LAD, LCX, and RCA by applying machine learning algorithms on the Z-Alizadeh Sani dataset that comprised 303 subjects, each with 54 features. In addition, our new methodology is developed to handle model uncertainty in the prediction of individual artery stenosis. It uses the hyperplane distance from a sample and accuracy rate of the classifier during the training phase to enhance its performance. Our results demonstrate high diagnostic performance of the proposed method for diagnosis of stenosis in individual RCA, LCX, and LAD, achieving accuracy rates of 82.67%, 83.67% and 86.43%, respectively. This is the best performance of ML techniques applied to the Z-Alizadeh Sani dataset.

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Authors and Affiliations

  1. Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia

    Roohallah Alizadehsani, Abbas Khosravi & Saeid Nahavandi

  2. Department of Engineering, Fasa Branch, Islamic Azad University, Post Box No 364, Fasa, Fars, 7461789818, Iran

    Mohamad Roshanzamir

  3. Département d’informatique, Université du Québec à Montréal, Montréal, Québec, Canada

    Moloud Abdar

  4. Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia

    Adham Beykikhoshk

  5. University of Texas at Dallas, Dallas, USA

    Mohammad Hossein Zangooei

  6. National Heart Centre Singapore, Singapore, Singapore

    Ru San Tan

  7. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya

  8. Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore

    U. Rajendra Acharya

  9. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    U. Rajendra Acharya

Authors
  1. Roohallah Alizadehsani
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  2. Mohamad Roshanzamir
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  3. Moloud Abdar
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  4. Adham Beykikhoshk
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  5. Mohammad Hossein Zangooei
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  6. Abbas Khosravi
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  7. Saeid Nahavandi
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  8. Ru San Tan
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  9. U. Rajendra Acharya
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Correspondence to Roohallah Alizadehsani.

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Communicated by V. Loia.

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Alizadehsani, R., Roshanzamir, M., Abdar, M. et al. Model uncertainty quantification for diagnosis of each main coronary artery stenosis. Soft Comput 24, 10149–10160 (2020). https://doi.org/10.1007/s00500-019-04531-0

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  • DOI: https://doi.org/10.1007/s00500-019-04531-0

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