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dc.contributor.authorÖzkan, Haydar
dc.date.accessioned2020-11-25T08:33:53Z
dc.date.available2020-11-25T08:33:53Z
dc.date.issued2016en_US
dc.identifier.citationÖZKAN, Haydar. "A Comparison of Classification Methods for Telediagnosis of Parkinson's Disease." Entropy, 18.4 (2016).en_US
dc.identifier.urihttps://hdl.handle.net/11352/3219
dc.description.abstractParkinson’s disease (PD) is a progressive and chronic nervous system disease that impairs the ability of speech, gait, and complex muscle-and-nerve actions. Early diagnosis of PD is quite important for alleviating the symptoms. Cost effective and convenient telemedicine technology helps to distinguish the patients with PD from healthy people using variations of dysphonia, gait or motor skills. In this study, a novel telemedicine technology was developed to detect PD remotely using dysphonia features. Feature transformation and several machine learning (ML) methods with 2-, 5- and 10-fold cross-validations were implemented on the vocal features. It was observed that the combination of principal component analysis (PCA) as a feature transformation (FT) and k-nearest neighbor (k-NN) as a classifier with 10-fold cross-validation has the best accuracy as 99.1%. All ML processes were applied to the prerecorded PD dataset using a newly created program named ParkDet 2.0. Additionally, the blind test interface was created on the ParkDet so that users could detect new patients with PD in future. Clinicians or medical technicians, without any knowledge of ML, will be able to use the blind test interface to detect PD at a clinic or remote location utilizing internet as a telemedicine application.en_US
dc.language.isoengen_US
dc.publisherMultidisciplinary Digital Publishing Instituteen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectTelemedicineen_US
dc.subjectParkinson’s Diseaseen_US
dc.subjectMachine Learningen_US
dc.subjectFeature Transformationen_US
dc.subjectPrincipal Component Analysisen_US
dc.subjectK-Nearest Neighboren_US
dc.titleA Comparison of Classification Methods for Telediagnosis of Parkinson's Diseaseen_US
dc.typearticleen_US
dc.contributor.departmentFSM Vakıf Üniversitesi, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümüen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.institutionauthorÖzkan, Haydar


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