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<p>Classification of fallers in Parkinson’s disease (PD) is challenging due to the heterogenous motor and non-motor symptoms. We developed a machine learning model integrating clinical and gait data to identify key clinical markers of faller status in PD. Of 468 participants, 396 with complete data were analyzed, with 298 assigned to training from one center and 98 to external validation from the other. Clinical assessments and GAITRite-derived gait metrics were obtained. Fall history classified participants as PD fallers, PD non-fallers, or healthy controls. Features were selected through statistical and importance-based approaches, and seven machine learning algorithms were trained. The Extra Trees classifier utilizing statistics-based feature selection demonstrated the highest performance (accuracy 88% internal, 89% external). Three principal domains consistently emerged: fear of falling (FoF), balance/gait measures (stride length, velocity, 360° rotation), and autonomic dysfunction. These findings support the feasibility of externally validated, multidomain machine learning–based faller classification in PD.</p>

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Classification of fallers in Parkinson’s disease through machine learning based feature analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月12日

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  • Minkyung Kim,
  • Sumin Kim,
  • MyungJin Chung,
  • Jin Whan Cho,
  • Hakje Yoo,
  • Jinyoung Youn

摘要

Classification of fallers in Parkinson’s disease (PD) is challenging due to the heterogenous motor and non-motor symptoms. We developed a machine learning model integrating clinical and gait data to identify key clinical markers of faller status in PD. Of 468 participants, 396 with complete data were analyzed, with 298 assigned to training from one center and 98 to external validation from the other. Clinical assessments and GAITRite-derived gait metrics were obtained. Fall history classified participants as PD fallers, PD non-fallers, or healthy controls. Features were selected through statistical and importance-based approaches, and seven machine learning algorithms were trained. The Extra Trees classifier utilizing statistics-based feature selection demonstrated the highest performance (accuracy 88% internal, 89% external). Three principal domains consistently emerged: fear of falling (FoF), balance/gait measures (stride length, velocity, 360° rotation), and autonomic dysfunction. These findings support the feasibility of externally validated, multidomain machine learning–based faller classification in PD.