Diagnostic estimation of OSAS using binary mixture logistic regression

dc.authorscopusid58062717700
dc.authorscopusid6603490446
dc.authorscopusid23973331200
dc.contributor.authorKaya Y.
dc.contributor.authorTa?luk M.E.
dc.contributor.authorSezgin N.
dc.date.accessioned2024-08-04T20:04:00Z
dc.date.available2024-08-04T20:04:00Z
dc.date.issued2012
dc.departmentİnönü Üniversitesien_US
dc.description2012 20th Signal Processing and Communications Applications Conference, SIU 2012 -- 18 April 2012 through 20 April 2012 -- Fethiye, Mugla -- 90786en_US
dc.description.abstractBinary (Binomial) Logistic Regression is a statistical model that can be used for classification. Concerning the targeted outcome, if the variance of observations is higher than the variance of expectations, because of overdispersion the success rate of the method in classification goes down. This overdispersion is thought as arising from the unobserved heterogen samples in the data set. In Composite models, the overdispersion is minimized by clustering the data into homogeneous subsets and performing a subset based process. In this study a composite binary logistic regression was used for estimating the sleep apnea. Through this model, snoring signals were classified and with a 98.16% success rate the apnea was diagnosed. © 2012 IEEE.en_US
dc.identifier.doi10.1109/SIU.2012.6204663
dc.identifier.isbn9781467300568
dc.identifier.scopus2-s2.0-84863459112en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/SIU.2012.6204663
dc.identifier.urihttps://hdl.handle.net/11616/92274
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.relation.ispartof2012 20th Signal Processing and Communications Applications Conference, SIU 2012, Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMixture Modelsen_US
dc.subjectOSASen_US
dc.subjectSignal Processingen_US
dc.titleDiagnostic estimation of OSAS using binary mixture logistic regressionen_US
dc.title.alternativeUyku apnesi?ni?n i?ki? durumlu kompozi?t loji?sti?k regresyon i?le tespi?ti?en_US
dc.typeConference Objecten_US

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