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dc.contributor.authorNilashi, Mehrbakhsh
dc.contributor.authorIbrahim, Othman
dc.contributor.authorAhani, Ali
dc.date.accessioned2020-02-27T03:16:53Z
dc.date.available2020-02-27T03:16:53Z
dc.date.issued2016
dc.identifier.issn2045-2322
dc.identifier.doi10.1038/srep34181
dc.identifier.urihttp://hdl.handle.net/10072/391963
dc.description.abstractParkinson’s disease (PD) is a member of a larger group of neuromotor diseases marked by the progressive death of dopamineproducing cells in the brain. Providing computational tools for Parkinson disease using a set of data that contains medical information is very desirable for alleviating the symptoms that can help the amount of people who want to discover the risk of disease at an early stage. This paper proposes a new hybrid intelligent system for the prediction of PD progression using noise removal, clustering and prediction methods. Principal Component Analysis (PCA) and Expectation Maximization (EM) are respectively employed to address the multi-collinearity problems in the experimental datasets and clustering the data. We then apply Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for prediction of PD progression. Experimental results on public Parkinson’s datasets show that the proposed method remarkably improves the accuracy of prediction of PD progression. The hybrid intelligent system can assist medical practitioners in the healthcare practice for early detection of Parkinson disease.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherNature Publishing Group
dc.relation.ispartofissue1
dc.relation.ispartofjournalScientific Reports
dc.relation.ispartofvolume6
dc.subject.fieldofresearchClinical sciences
dc.subject.fieldofresearchNeurology and neuromuscular diseases
dc.subject.fieldofresearchcode3202
dc.subject.fieldofresearchcode320905
dc.subject.keywordsScience & Technology
dc.subject.keywordsMultidisciplinary Sciences
dc.subject.keywordsScience & Technology - Other Topics
dc.subject.keywordsSYSTEM
dc.subject.keywordsCLASSIFICATION
dc.titleAccuracy Improvement for Predicting Parkinson's Disease Progression
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationNilashi, M; Ibrahim, O; Ahani, A, Accuracy Improvement for Predicting Parkinson's Disease Progression, Scientific Reports, 2016, 6 (1)
dcterms.dateAccepted2016-09-06
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-02-27T03:14:52Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s) 2016. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
gro.hasfulltextFull Text
gro.griffith.authorAhani, Ali


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