Comparison of Supervised Machine Learning Algorithms for Classifying of Home Discharge Possibility in Convalescent Stroke Patients: A Secondary Analysis.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Published Date: Jul 26, 2021

Abstract

OBJECTIVES: Classifying the possibility of home discharge is important during stroke rehabilitation to support decision-making. There have been several studies on supervised machine learning algorithms, but only a few have compared the performance of different algorithms based on the same dataset for the classification of home discharge possibility. Therefore, we aimed to evaluate five supervised machine learning algorithms for the classification of home discharge possibility in stroke patients.

Authors

  • Takeshi Imura
    Department of Rehabilitation, Araki Neurosurgical Hospital, Hiroshima, Japan.
  • Haruki Toda
    Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo Japan.
  • Yuji Iwamoto
    Department of Rehabilitation, Araki Neurosurgical Hospital, Hiroshima, Japan. Electronic address: yuji_ooooot@yahoo.co.jp.
  • Tetsuji Inagawa
    Department of Neurosurgery, Araki Neurosurgical Hospital, Hiroshima, Japan.
  • Naoki Imada
    Department of Rehabilitation, Araki Neurosurgical Hospital, Hiroshima, Japan.
  • Ryo Tanaka
    Graduate School of Humanities and Social Sciences, Hiroshima University, Japan.
  • Yu Inoue
    Graduate School of Humanities and Social Sciences, Hiroshima University, Japan.
  • Hayato Araki
    Department of Neurosurgery, Araki Neurosurgical Hospital, Hiroshima, Japan.
  • Osamu Araki
    Department of Neurosurgery, Araki Neurosurgical Hospital, Hiroshima, Japan.

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