Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3845
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dc.contributor.authorDastjerd, Niousha Karim-
dc.contributor.authorSert, Onur Can-
dc.contributor.authorÖzyer, Tansel-
dc.contributor.authorAlhajj, Reda-
dc.date.accessioned2020-10-22T16:40:34Z-
dc.date.available2020-10-22T16:40:34Z-
dc.date.issued2019
dc.identifier.citationDastjerd, N. K., Sert, O. C., Ozyer, T and Alhajj, R. (2019). Fuzzy classification methods based diagnosis of Parkinson’s disease from speech test cases. Current Aging Science, 12(2), 100-120.en_US
dc.identifier.issn18746098
dc.identifier.urihttps://hdl.handle.net/20.500.11851/3845-
dc.identifier.urihttps://www.eurekaselect.com/172984/article-
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. © 2019 Bentham Science Publishers.en_US
dc.language.isoenen_US
dc.publisherBentham Science Publishersen_US
dc.relation.ispartofCurrent Aging Scienceen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAdaptive neuro fuzzy classificationen_US
dc.subjectdata miningen_US
dc.subjectfuzzy classificationen_US
dc.subjectmachine learningen_US
dc.subjectneuro fuzzy classificationen_US
dc.subjectParkinson’s diseaseen_US
dc.titleFuzzy classification methods based diagnosis of parkinson’s disease from speech test casesen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Artificial Intelligence Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Yapay Zeka Mühendisliği Bölümütr_TR
dc.identifier.volume12
dc.identifier.issue2
dc.identifier.startpage100
dc.identifier.endpage120
dc.authorid0000-0002-2529-5533-
dc.identifier.scopus2-s2.0-85075188018en_US
dc.institutionauthorÖzyer, Tansel-
dc.identifier.pmid31241024en_US
dc.identifier.doi10.2174/1874609812666190625140311-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ3-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeArticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering
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