Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach.

Journal: Journal of neuroengineering and rehabilitation
Published Date: Jan 29, 2024

Abstract

BACKGROUND: Computerized posturography obtained in standing conditions has been applied to classify fall risk for older adults or disease groups. Combining machine learning (ML) approaches is superior to traditional regression analysis for its ability to handle complex data regarding its characteristics of being high-dimensional, non-linear, and highly correlated. The study goal was to use ML algorithms to classify fall risks in community-dwelling older adults with the aid of an explainable artificial intelligence (XAI) approach to increase interpretability.

Authors

  • Huey-Wen Liang
    Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan, ROC.
  • Rasoul Ameri
    Department of Information Management, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC.
  • Shahab Band
    International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC. shahab@yuntech.edu.tw.
  • Hsin-Shui Chen
    Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital Yulin Branch, Douliu, Taiwan, ROC. p10744018@emba.ntu.edu.tw.
  • Sung-Yu Ho
    Department of Information Management, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC.
  • Bilal Zaidan
    International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC.
  • Kai-Chieh Chang
    Department of Neurology, National Taiwan University Hospital Yulin Branch, Douliu, Taiwan, ROC.
  • Arthur Chang
    Bachelor Program in Interdisciplinary Studies, National Yunlin University of Science and Technology, Douliu, Taiwan.

Keywords

External Resources

Popular Topics
Recent Journals