Factors contributing to chronic ankle instability in parcel delivery workers based on machine learning techniques.

Journal: BMC medical informatics and decision making
PMID: 39948497

Abstract

BACKGROUND: Ankle injuries in parcel delivery workers (PDWs) are most often caused by trips. Ankle sprains have high recurrence rates and are associated with chronic ankle instability (CAI). This study aimed to develop, determine, and compare the predictive performance of statistical machine learning models to classify PDWs with and without CAI using postural control, ankle range of motion, ankle joint muscle strength, and anatomical deformity variables.

Authors

  • Ui-Jae Hwang
    Laboratory of KEMA AI Research (KAIR), Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea.
  • Oh-Yun Kwon
    Laboratory of Kinetic Ergocise Based on Movement Analysis, Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea.
  • Jun-Hee Kim
    Laboratory of KEMA AI Research (KAIR), Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea.
  • Gyeong-Tae Gwak
    Laboratory of KEMA AI Research (KAIR), Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea.

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