Using machine learning to identify Parkinson's disease severity subtypes with multimodal data.

Journal: Journal of neuroengineering and rehabilitation
Published Date: Jun 2, 2025

Abstract

BACKGROUND: Classifying and predicting Parkinson's disease (PD) is challenging because of its diverse subtypes based on severity levels. Currently, identifying objective biomarkers associated with disease severity that can distinguish PD subtypes in clinical trials is necessary. This study aims to address the clinical applicability and heterogeneity of PD using PD severity subtypes classification and digital biomarker development by combining objective multimodal data with machine learning (ML) approaches.

Authors

  • Hwayoung Park
    Department of Health Sciences, The Graduate School of Dong-A University, Busan, Republic of Korea.
  • Changhong Youm
    Department of Health Sciences, The Graduate School of Dong-A University, Busan, Republic of Korea. chyoum@dau.ac.kr.
  • Sang-Myung Cheon
    Department of Neurology, School of Medicine, Dong-A University, Busan, Republic of Korea.
  • Bohyun Kim
    Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. kbh@catholic.ac.kr.
  • Hyejin Choi
    Department of Health Sciences, The Graduate School of Dong-A University, Busan, Republic of Korea.
  • Juseon Hwang
    Department of Health Sciences, The Graduate School of Dong-A University, Busan, Republic of Korea.
  • Minsoo Kim
    Biomechanics Laboratory, Department of Healthcare and Science, College of Health Sciences, Dong-A University, 37 Nakdong‑daero, 550 Beon‑gil, Hadan 2-Dong, Saha-gu, Busan, 49315, Republic of Korea.

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