A Partial Least-Squares Regression Model to Measure Parkinson’s Disease Motor States Using Smartphone Data

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dc.contributor.authorMemedi, Mevludin
dc.contributor.authorAghanavesi, Somayeh
dc.date.accessioned2020-01-04T07:21:38Z
dc.date.available2020-01-04T07:21:38Z
dc.date.issued2020-01-07
dc.description.abstractDesign choices related to development of data-driven models significantly impact or degrade predictive performance of the models. One of the essential steps during development and evaluation of such models is the choice of feature selection and dimension reduction techniques. That is imperative especially in cases dealing with multimodal data gathered from different sources. In this paper, we will investigate the behavior of Partial Least Squares (PLS) regression for dimension reduction and prediction of motor states of Parkinson’s disease (PD) patients, using upper limb motor data gathered by means of a smartphone. The results in terms of correlations between smartphone-based and clinician-derived scores were compared to a previous study using the same data where principal component analysis (PCA) and support vector machines (SVM) were used. The findings from this study show that PLS is superior in terms of prediction performance of motor states in PD than combining PCA and SVM. This indicates that PLS could be considered as a useful methodology in problems where data-driven analysis is needed.
dc.format.extent7 pages
dc.identifier.doi10.24251/HICSS.2020.131
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/63870
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData, Text, and Web Mining for Business Analytics
dc.subjectdata mining
dc.subjectdimension reduction
dc.subjecte-health
dc.subjectfeature selection
dc.subjectparkinson's disease
dc.subjectpredictive performance
dc.titleA Partial Least-Squares Regression Model to Measure Parkinson’s Disease Motor States Using Smartphone Data
dc.typeConference Paper
dc.type.dcmiText

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