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A partial least-squares regression model to measure Parkinson’s disease motor states using smartphone data
Örebro University, Örebro University School of Business. (Informatics)ORCID iD: 0000-0002-2372-4226
Computer Engineering, Dalarna University, Sweden.
2020 (English)In: Proceedings of the 53rd Hawaii International Conference on System Sciences | 2020, Maui, Hawaii: HCSS , 2020, p. 1056-1062Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Maui, Hawaii: HCSS , 2020. p. 1056-1062
National Category
Computer and Information Sciences Probability Theory and Statistics
Research subject
Informatics
Identifiers
URN: urn:nbn:se:oru:diva-78958OAI: oai:DiVA.org:oru-78958DiVA, id: diva2:1384833
Conference
HICSS 53, Grand Wailea, Maui, January 7-10, 2020
Funder
Knowledge FoundationAvailable from: 2020-01-11 Created: 2020-01-11 Last updated: 2020-02-24Bibliographically approved

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A partial least-squares regression model to measure Parkinson’s disease motor states using smartphone data

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Output format
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