Predicting stroke severity of patients using interpretable machine learning algorithms.

Journal: European journal of medical research
PMID: 39538301

Abstract

BACKGROUND: Stroke is a significant global health concern, ranking as the second leading cause of death and placing a substantial financial burden on healthcare systems, particularly in low- and middle-income countries. Timely evaluation of stroke severity is crucial for predicting clinical outcomes, with standard assessment tools being the Rapid Arterial Occlusion Evaluation (RACE) and the National Institutes of Health Stroke Scale (NIHSS). This study aims to utilize Machine Learning (ML) algorithms to predict stroke severity using these two distinct scales.

Authors

  • Amir Sorayaie Azar
    Department of Computer Engineering, Urmia University, Urmia, Iran.
  • Tahereh Samimi
    Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran.
  • Ghanbar Tavassoli
    Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran.
  • Amin Naemi
    Centre of Health Informatics and Technology, The Maersk Mc-Kinney Moller, Institute, University of Southern Denmark, Odense, Denmark.
  • Bahlol Rahimi
    Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran.
  • Zahra Hadianfard
    Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran.
  • Uffe Kock Wiil
    Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Denmark.
  • Surena Nazarbaghi
    Department of Neurology, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran.
  • Jamshid Bagherzadeh Mohasefi
    Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran; Department of Electrical and Computer Engineering, Urmia University, Urmia, Iran. Electronic address: j.bagherzadeh@urmia.ac.ir.
  • Hadi Lotfnezhad Afshar
    Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran. lotfnezhadafshar.h@umsu.ac.ir.

Keywords

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