Pairwise Classifier Approach to Automated Diagnosis of Disorder Degree of Obstructive Sleep Apnea Syndrome: Combining of AIRS and One versus One (OVO-AIRS)

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dc.contributor.authorPolat, K.
dc.contributor.authorGuenes, S.
dc.contributor.authorYosunkaya, S.
dc.date.accessioned2020-03-26T17:39:32Z
dc.date.available2020-03-26T17:39:32Z
dc.date.issued2009
dc.departmentSelçuk Üniversitesien_US
dc.descriptionInternational Multi-Conference of Engineers and Computer Scientists -- MAR 18-20, 2009 -- Kowloon, PEOPLES R CHINAen_US
dc.description.abstractArtificial Immune Recognition System (AIRS) is an immune inspired supervised classification algorithm and also works in classifying of multi class datasets. But the performance of AIRS classifier in classifying multi class datasets is generally lower than its performance in case of classifying two class datasets. In order to overcome this problem, we have combined the one-versus-one (OVO) and AIRS in the diagnosis of disorder degree of obstructive sleep apnea syndrome (OSAS) that affects both the right and the left cardiac ventricle. The OSAS dataset consists of four classes including of normal (25 subjects), mild OSAS (AM (Apnea Apnea and Hypoapnea Index)=5-15 and 14 subjects), moderate OSAS (AHI<15-30 and 18 subjects), and serious OSAS (AHI>30 and 26 subjects). In the extracting of features that is characterized the OSAS disease, the clinical features obtained from Polysomnography used diagnostic tool for obstructive sleep apnea in patients clinically suspected of suffering from this disease have been used. The used clinical features are Arousals Index (ARI), Apnea and Hypoapnea Index (AHI), SaO2 minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO2 intervals bigger than 89%. We have used two fold cross validation to split OSAS dataset and also used the classification accuracy, sensitivity- specificity analysis, and confusion matrix to evaluate the performance of proposed method. While AIRS algorithm obtained 90.24% classification accuracy, the proposed method based on AIRS algorithm and OVO achieved 98.24% classification accuracy. These results show that the proposed method can confidently be used in the determining of disorder degree of OSAS.en_US
dc.description.sponsorshipInt Assoc Engineers, IAENG, Soc Artificial Intelligence, IAENG, Soc Bioinformat, IAENG, Soc Comp Sci, IAENG, Soc Data Min, IAENG, Soc Elect Engn, IAENG, Soc Imaging Engn, IAENG, Soc Ind Engn, IAENG, Soc Informat Syst Engn, IAENG, Soc Internet Comp & Web Serv, IAENG, Soc Mech Engn, IAENG, Soc Operat Res, IAENG, Soc Sci Comp, IAENG, Soc Software Engn, IAENG, Soc Wireless Networksen_US
dc.description.sponsorshipThe Scientific of Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [108E033]; Scientific Research Project of Selcuk UniversitySelcuk Universityen_US
dc.description.sponsorshipManuscript received October 30, 2008. This work was supported by the The Scientific of Technological Research Council of Turkey (TUBITAK) (Project number: 108E033). And also this study has been supported by Scientific Research Project of Selcuk University.en_US
dc.identifier.endpage+en_US
dc.identifier.isbn978-988-17012-2-0
dc.identifier.issn2078-0958en_US
dc.identifier.startpage214en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/23740
dc.identifier.wosWOS:000266097200040en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherINT ASSOC ENGINEERS-IAENGen_US
dc.relation.ispartofIMECS 2009: INTERNATIONAL MULTI-CONFERENCE OF ENGINEERS AND COMPUTER SCIENTISTS, VOLS I AND IIen_US
dc.relation.ispartofseriesLecture Notes in Engineering and Computer Science
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial Immune Recognition Systemen_US
dc.subjectOne versus Oneen_US
dc.subjectObstructive sleep apnea syndrome (OSAS)en_US
dc.subjectPolysomnographyen_US
dc.titlePairwise Classifier Approach to Automated Diagnosis of Disorder Degree of Obstructive Sleep Apnea Syndrome: Combining of AIRS and One versus One (OVO-AIRS)en_US
dc.typeConference Objecten_US

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