Interpretable machine learning for differentiating SCA3 and MSA-C using gait and postural features from wearable sensors

Abstract Background Spinocerebellar ataxia type 3 (SCA3) and the cerebellar subtype of multiple system atrophy (MSA-C) both manifest with cerebellar ataxia and postural instability in the early stages, leading to considerable clinical overlap and frequent misdiagnosis or delayed intervention. Curren...

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Ngā kaituhi matua:	Yuanyuan Xiao, Kailiang Luo, Yue Zhang, Wanli Zhang, QiKui Sun, Bingwei He, ShiRui Gan, Xinyuan Chen
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	BMC 2025-12-01
Rangatū:	Journal of NeuroEngineering and Rehabilitation
Ngā marau:	Spinocerebellar ataxia Multiple system atrophy Center of pressure Gait analysis Machine learning SHAP
Urunga tuihono:	https://doi.org/10.1186/s12984-025-01843-1
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Interpretable machine learning for differentiating SCA3 and MSA-C using gait and postural features from wearable sensors

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