A new hybrid intelligent system for accurate detection of Parkinson's disease

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dc.authorid0000-0002-8929-3473en_US
dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0002-3527-8825en_US
dc.contributor.authorHariharan, Muthusamy
dc.contributor.authorPolat, Kemal
dc.contributor.authorSindhu, Ravindran
dc.date.accessioned2021-06-23T19:36:18Z
dc.date.available2021-06-23T19:36:18Z
dc.date.issued2014
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractElderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset. (C) 2014 Elsevier Ireland Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.cmpb.2014.01.004
dc.identifier.endpage913en_US
dc.identifier.issn0169-2607
dc.identifier.issn1872-7565
dc.identifier.issue3en_US
dc.identifier.pmid24485390en_US
dc.identifier.scopus2-s2.0-84894268248en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage904en_US
dc.identifier.urihttps://doi.org/10.1016/j.cmpb.2014.01.004
dc.identifier.urihttps://hdl.handle.net/20.500.12491/7967
dc.identifier.volume113en_US
dc.identifier.wosWOS:000331726500017en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.ispartofComputer Methods And Programs In Biomedicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectParkinson's Diseaseen_US
dc.subjectDysphonia Featuresen_US
dc.subjectFeature Weightingen_US
dc.subjectFeature Selectionen_US
dc.subjectClassificationen_US
dc.titleA new hybrid intelligent system for accurate detection of Parkinson's diseaseen_US
dc.typeArticleen_US

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