Prediction of poststroke independent walking using machine learning: a retrospective study.

Journal: BMC neurology
Published Date: Sep 10, 2024

Abstract

BACKGROUND: Accurately predicting the walking independence of stroke patients is important. Our objective was to determine and compare the performance of logistic regression (LR) and three machine learning models (eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest (RF)) in predicting walking independence at discharge in stroke patients, as well as to explore the variables that predict prognosis.

Authors

  • Zhiqing Tang
    School of Rehabilitation, Capital Medical University, Beijing, China; Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China.
  • Wenlong Su
    School of Rehabilitation, Capital Medical University, Beijing, China; Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China; University of Health and Rehabilitation Sciences, Shandong Province, China.
  • Tianhao Liu
    School of Rehabilitation, Capital Medical University, Beijing, China; Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China.
  • Haitao Lu
    School of Rehabilitation, Capital Medical University, 10 Jiaomen North Road, Fengtai District, Beijing, 100068, China.
  • Ying Liu
    The First School of Clinical Medicine, Lanzhou University, Lanzhou, China.
  • Hui Li
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Kaiyue Han
    School of Rehabilitation, Capital Medical University, 10 Jiaomen North Road, Fengtai District, Beijing, 100068, China.
  • Md Moneruzzaman
    School of Rehabilitation, Capital Medical University, 10 Jiaomen North Road, Fengtai District, Beijing, 100068, China.
  • Junzi Long
    School of Rehabilitation, Capital Medical University, 10 Jiaomen North Road, Fengtai District, Beijing, 100068, China.
  • Xingxing Liao
    School of Rehabilitation, Capital Medical University, 10 Jiaomen North Road, Fengtai District, Beijing, 100068, China.
  • Xiaonian Zhang
    School of Rehabilitation, Capital Medical University, Beijing, China; Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China.
  • Lei Shan
    School of Rehabilitation, Capital Medical University, 10 Jiaomen North Road, Fengtai District, Beijing, 100068, China.
  • Hao Zhang
    College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.

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