Comparison of dimension reduction methods using patient satisfaction data

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dc.authoridAktürk, Zekeriya/0000-0002-9772-3285
dc.authorwosidAktürk, Zekeriya/ABF-6876-2021
dc.contributor.authorTure, Mevlut
dc.contributor.authorKurt, Imran
dc.contributor.authorAkturk, Zekeriya
dc.date.accessioned2024-06-12T11:12:53Z
dc.date.available2024-06-12T11:12:53Z
dc.date.issued2007
dc.departmentTrakya Üniversitesien_US
dc.description.abstractIn this study, we compared classical principal components analysis (PCA), generalized principal components analysis (GPCA), linear principal components analysis using neural networks (PCA-NN), and non-linear principal components analysis using neural networks (NLPCA-NN). Data were extracted from the patient satisfaction query with regard to the satisfaction of patients from hospital staff, which was applied in 2005 at the outpatient clinics of Trakya University Medical Faculty. We found that percentages of explained variance of principal components from PCA-NN and NLPCA-NN were highest for doctor, nurse, radiology technician, laboratory technician, and other staff using a patient satisfaction data set. Results show that methods using NN which have higher percentages of explained variances than classical methods could be used for dimension reduction. (C) 2005 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2005.12.003
dc.identifier.endpage426en_US
dc.identifier.issn0957-4174
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-33750438421en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage422en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2005.12.003
dc.identifier.urihttps://hdl.handle.net/20.500.14551/23348
dc.identifier.volume32en_US
dc.identifier.wosWOS:000242979100017en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPrincipal Components Analysisen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectGeneralized Principal Components Analysisen_US
dc.subjectDimension Reductionen_US
dc.subjectPatient Satisfactionen_US
dc.subjectPrincipal Component Analysisen_US
dc.subjectNeural-Networksen_US
dc.subjectSystemen_US
dc.titleComparison of dimension reduction methods using patient satisfaction dataen_US
dc.typeArticleen_US

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