Predicting the conversion from clinically isolated syndrome to multiple sclerosis: An explainable machine learning approach.

Journal: Multiple sclerosis and related disorders
PMID: 38642495

Abstract

INTRODUCTION: Predicting the conversion of clinically isolated syndrome (CIS) to clinically definite multiple sclerosis (CDMS) is critical to personalizing treatment planning and benefits for patients. The aim of this study is to develop an explainable machine learning (ML) model for predicting this conversion based on demographic, clinical, and imaging data.

Authors

  • Saeid Rasouli
    School of Medicine, Five Senses Health Research Institute, Hazrat-e Rasool General Hospital, Iran University of Medical Sciences, Tehran, Iran. Electronic address: rasoulisaeid92@gmail.com.
  • Mohammad Sedigh Dakkali
    Department of Ophthalmology, School of Medicine, Al Zahra Eye Hospital, Zahedan University of Medical Sciences, Zahedan, Iran.
  • Reza Azarbad
    Cellular and Molecular Biology Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran.
  • Azim Ghazvini
    School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
  • Mahdi Asani
    Department of Ophthalmology, School of Medicine, Al Zahra Eye Hospital, Zahedan University of Medical Sciences, Zahedan, Iran.
  • Zahra Mirzaasgari
    Department of Neurology, Firoozgar hospital, School of medicine, University of Medical Science, Iran.
  • Mohammed Arish
    Department of Ophthalmology, School of Medicine, Al Zahra Eye Hospital, Zahedan University of Medical Sciences, Zahedan, Iran.

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