GSVMA: A Genetic Support Vector Machine ANOVA Method for CAD Diagnosis.

Journal: Frontiers in cardiovascular medicine
Published Date: Feb 4, 2022

Abstract

BACKGROUND: Coronary artery disease (CAD) is one of the crucial reasons for cardiovascular mortality in middle-aged people worldwide. The most typical tool is angiography for diagnosing CAD. The challenges of CAD diagnosis using angiography are costly and have side effects. One of the alternative solutions is the use of machine learning-based patterns for CAD diagnosis.

Authors

  • Javad Hassannataj Joloudari
    Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
  • Faezeh Azizi
    Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
  • Mohammad Ali Nematollahi
    Department of Computer Sciences, Fasa University, Fasa, Iran.
  • Roohallah Alizadehsani
    Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia.
  • Edris Hassannatajjeloudari
    Department of Nursing, School of Nursing and Allied Medical Sciences, Maragheh Faculty of Medical Sciences, Maragheh, Iran.
  • Issa Nodehi
    Department of Computer Engineering, University of Qom, Qom, Iran.
  • Amir Mosavi
    Faculty of Informatics, Technische Universität Dresden, Dresden, Germany.

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