A hierarchical machine learning model based on Glioblastoma patients' clinical, biomedical, and image data to analyze their treatment plans.

Journal: Computers in biology and medicine
Published Date: Oct 4, 2022

Abstract

AIM OF STUDY: Glioblastoma Multiforme (GBM) is an aggressive brain cancer in adults that kills most patients in the first year due to ineffective treatment. Different clinical, biomedical, and image data features are needed to analyze GBM, increasing complexities. Besides, they lead to weak performances for machine learning models due to ignoring physicians' knowledge. Therefore, this paper proposes a hierarchical model based on Fuzzy C-mean (FCM) clustering, Wrapper feature selection, and twelve classifiers to analyze treatment plans.

Authors

  • Mohammad Mahdi Ershadi
    Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran. Electronic address: ershadi.mm1372@aut.ac.ir.
  • Zeinab Rahimi Rise
    Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran. Electronic address: zeinab.rahimi@aut.ac.ir.
  • Seyed Taghi Akhavan Niaki
    Department of Industrial Engineering, Sharif University of Technology, PO Box 11155-9414, Tehran, 1458889694, Iran. Electronic address: Niaki@Sharif.edu.

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