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

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Title:A Partial Least-Squares Regression Model to Measure Parkinson’s Disease Motor States Using Smartphone Data
Authors:Memedi, Mevludin
Aghanavesi, Somayeh
Keywords:Data, Text, and Web Mining for Business Analytics
data mining
dimension reduction
e-health
feature selection
show 2 moreparkinson's disease
predictive performance
show less
Date Issued:07 Jan 2020
Abstract:Design 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.
Pages/Duration:7 pages
URI:http://hdl.handle.net/10125/63870
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.131
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Data, Text, and Web Mining for Business Analytics


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