A Multi-view Classification Framework for Falls Prediction: Multiple-domain Assessments in Parkinson’s Disease

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dc.contributor.authorHuang, Xiuyu
dc.contributor.authorMark, Latt
dc.contributor.authorMatloob, Khushi
dc.contributor.authorPelicioni, Paulo
dc.contributor.authorBrodie, Matthew
dc.contributor.authorLord , Stephen
dc.contributor.authorLoy, Clement
dc.contributor.authorPoon, Simon
dc.date.accessioned2020-12-24T19:41:56Z
dc.date.available2020-12-24T19:41:56Z
dc.date.issued2021-01-05
dc.description.abstractFalls are one of the most common causes of injury and disability in people with Parkinson’s disease (PD). This study developed an augmented machine learning framework for screening the risk of falling in people with PD using multiple domain assessments. A sample of 109 people with PD (50 fallers and 59 non-fallers) undertook four domains of assessment: disease-specific rating scales, clinical examination measures, physiological assessments, and gait analysis. A multi-view classifying framework was developed from a sequence of procedures and achieved 77.50% average predicting accuracy. The robustness of the multi-view framework was tested by comparing outcomes of three different view selection methods. The developed framework may have implications for clinical decision making, as some of the PD fall risk variables/features may be amenable to treatment. Our results showed that external reliability can be achieved by a simple voting mechanism from multiple, perhaps diverse, perspective consensus.
dc.format.extent9 pages
dc.identifier.doi10.24251/HICSS.2021.413
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/71029
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th 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.subjectBig Data on Healthcare Application
dc.subjectclinical decision making
dc.subjectfall prediction
dc.subjectmachine learning
dc.subjectmulti-view classification
dc.subjectparkinson's disease
dc.titleA Multi-view Classification Framework for Falls Prediction: Multiple-domain Assessments in Parkinson’s Disease
prism.startingpage3398

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