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Parkinson's Disease and UPDRS-III Prediction Using Quiet Standing Data and Applied Machine Learning

Parkinson's disease (PD) is a neurodegenerative disease that affects motor abilities with increasing severity as the disease progresses. Traditional methods for diagnosing PD require specialists scoring qualitative symptoms using the motor subscale of the Unified Parkinson's Disease Rating Scale (UPDRS-III). Using force-plate data during quiet standing (QS), this study uses machine learning to target the characterization and prediction of PD and UPDRS-III. The purpose of predicting different subscores of the UPDRS-III is to give specialists more tools to help make an informed diagnosis and prognosis. The classification models employed classified PD with a sensitivity of 87.5% and specificity of 83.1%. Stepwise forward regression indicated that features correlated with base of support were most useful in the prediction of head rigidity (r-square = .753). Although there is limited data, this thesis can be used as an exploratory study that evaluates the predictability of UPDRS-III subscores using QS data. Similar prediction models can be implemented to a home setting using low-cost force plates as a novel telemedicine technique to track disease progression.

Identiferoai:union.ndltd.org:unt.edu/info:ark/67531/metadc1808425
Date05 1900
CreatorsExley, Trevor Wayne
ContributorsPatterson, Rita M., Albert, Mark V., Vaidyanathan, Vijay
PublisherUniversity of North Texas
Source SetsUniversity of North Texas
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation
Formatx, 43 pages, Text
RightsPublic, Exley, Trevor Wayne, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved.

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