Multi-Class F-Score Feature Selection Approach to Classification of Obstructive Sleep Apnea Syndrome

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dc.contributor.authorGüneş, Salih
dc.contributor.authorPolat, Kemal
dc.contributor.authorYosunkaya, Şebnem
dc.date.accessioned2020-03-26T18:04:42Z
dc.date.available2020-03-26T18:04:42Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this paper, a new feature selection named as multi-class f-score feature selection is proposed for sleep apnea classification having different disorder degrees (mild OSAS, moderate OSAS, serious OSAS, and non-OSAS). f-Score is used to measure the discriminating power of features in the classification of two-class pattern recognition problems. In order to apply the f-score feature selection to multi-class datasets, we have used the f-score feature selection as pairwise (in the form of two classes) in the diagnosis of obstructive sleep apnea syndrome (OSAS) with four classes. After feature selection process, MLPANN (Multi-layer perceptron artificial neural network) classifier is used to diagnose the OSAS having different disorder degrees. While MLPANN obtained 63.41% classification accuracy on the diagnosis of OSAS, the combination of MLPANN and multi-class f-score feature selection achieved 84.14% classification accuracy using 50-50% training-testing split of OSAS dataset with four classes. These results demonstrate that the proposed multi-class f-score feature selection method is effective and robust in determining the disorder degrees of OSAS.en_US
dc.description.sponsorshipThe Scientific of Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [108E033]; Selcuk UniversitySelcuk Universityen_US
dc.description.sponsorshipThis study was Supported by The Scientific of Technological Research Council of Turkey (TUBITAK) (Project number: 108E033) and also by the Scientific Research Projects of Selcuk University.en_US
dc.identifier.citationGüneş, S., Polat, K., Yosunkaya, Ş., (2010). Multi-Class F-Score Feature Selection Approach to Classification of Obstructive Sleep Apnea Syndrome. Expert Systems with Applications, 37(2), 998-1004. DOI: 10.1016/j.eswa.2009.05.075
dc.identifier.doi10.1016/j.eswa.2009.05.075en_US
dc.identifier.endpage1004en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage998en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2009.05.075
dc.identifier.urihttps://hdl.handle.net/20.500.12395/25088
dc.identifier.volume37en_US
dc.identifier.wosWOS:000272432300012en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorGüneş, Salih
dc.institutionauthorPolat, Kemal
dc.institutionauthorYosunkaya, Şebnem
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.selcuk20240510_oaigen_US
dc.subjectObstructive Sleep Apnea Syndrome (OSAS)en_US
dc.subjectMulti-Class F-Score Feature Selectionen_US
dc.subjectMulti-Layer Perceptron Artificial Neural Networken_US
dc.subjectPolysomnographyen_US
dc.titleMulti-Class F-Score Feature Selection Approach to Classification of Obstructive Sleep Apnea Syndromeen_US
dc.typeArticleen_US

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