Machine learning models for Parkinson's disease detection and stage classification based on spatial-temporal gait parameters.
Background: Parkinson's disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stag...
| Published in: | Gait & Posture Vol. 98; pp. 49 - 56 |
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| Main Authors: | , , , , |
| Format: | research Journal Article |
| Published: |
Elsevier B.V.
Oct2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160365112&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160365112 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09666362 3L6 jtl: Gait & Posture issn: 09666362 maglogo: N pubinfo: dt: Oct2022 vid: 98 pid: 1004 pub: Elsevier B.V. artinfo: ui: 160365112 160365112 NLM36049418 160365112 10.1016/j.gaitpost.2022.08.014 NLM36049418 160365112 ppf: 49 ppct: 7 formats: tig: atl: Machine learning models for Parkinson's disease detection and stage classification based on spatial-temporal gait parameters. aug: au: Ferreira, Marta Isabel A.S.N Barbieri, Fabio Augusto Moreno, Vinícius Christianini Penedo, Tiago Tavares, João Manuel R.S. affil: Faculdade de Engenharia, Universidade do Porto, Portugal sug: subj: Parkinson Disease Diagnosis Neurodegenerative Diseases Gait Probability Human ab: Background: Parkinson's disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research Question: This article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.Methods: Gait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.Results: In the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.Significance: The results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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