Fuzzy classification methods based diagnosis of Parkinson’s disease from speech test cases

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dc.authorid0000-0001-6657-9738
dc.contributor.authorDastjerd, Niousha Karimi
dc.contributor.authorSert, Onur Can
dc.contributor.authorÖzyer, Tansel
dc.contributor.authorAlhajj, Reda
dc.date.accessioned2019-12-27T06:58:53Z
dc.date.available2019-12-27T06:58:53Z
dc.date.issued2019
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractBackground: Together with the Alzheimer’s disease, Parkinson’s disease is considered as one of the two serious known neurodegenerative diseases. Physicians find it hard to predict whether a given patient has already developed or is expected to develop the Parkinson’s disease in the future. To overcome this difficulty, it is possible to develop a computing model, which analyzes the data related to a given patient and predicts with acceptable accuracy when he/she is anticipated to develop the Parkinson’s disease. Objectives: This paper contributes an attractive prediction framework based on some machine learning approaches for distinguishing people with Parkinsonism from healthy individuals. Methods: Several fuzzy classifiers such as Inductive Fuzzy Classifier, Fuzzy Rough Classifier and two types of neuro-fuzzy classifiers have been employed. Results: The fuzzy classifiers utilized in this study have been tested using the “Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set” of 40 subjects available on the UCI repository. Conclusion: The results achieved show that FURIA, MLP-Bagging-SGD, genfis2 and scg1 performed the best among the fuzzy rough, WEKA, adaptive neuro-fuzzy and neuro-fuzzy classifiers, respectively. The worst performance belongs to nearest neighborhood, IBK, genfis3 and scg3 among the formerly mentioned classifiers. The results reported in this paper are better in comparison to the results reported in Sakar et al., where the same dataset was used, with utilization of different classifiers. This demonstrates the applicability and effectiveness of the fuzzy classifiers used in this study as compared to the non-fuzzy classifiers used by Sakar et al.
dc.identifier.citationDastjerd, N. K., Sert, O. C., Özyer, T. ve Alhajj, R. (2019). Fuzzy classification methods based diagnosis of Parkinson’s disease from speech test cases. Current Aging Science, 12(2), 100-120. http://doi.org/10.2174/1874609812666190625140311
dc.identifier.doi10.2174/1874609812666190625140311
dc.identifier.endpage120
dc.identifier.issn1874-6098
dc.identifier.issn1874-6128
dc.identifier.issue2
dc.identifier.scopusqualityQ3
dc.identifier.startpage100
dc.identifier.urihttp://doi.org/10.2174/1874609812666190625140311
dc.identifier.urihttps://hdl.handle.net/20.500.12511/4755
dc.identifier.volume12
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherBentham Science Publishers
dc.relation.ispartofCurrent Aging Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectParkinson’s Disease
dc.subjectData Mining
dc.subjectMachine Learning
dc.subjectFuzzy Classification
dc.subjectNeuro Fuzzy Classification
dc.subjectAdaptive Neuro Fuzzy Classification
dc.titleFuzzy classification methods based diagnosis of Parkinson’s disease from speech test cases
dc.typeArticle

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