Comparison of different classifier algorithms on the automated detection of obstructive sleep apnea syndrome

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dc.contributor.authorPolat, Kemal
dc.contributor.authorYosunkaya, Sebnem
dc.contributor.authorGunes, Salih
dc.date.accessioned2020-03-26T17:26:32Z
dc.date.available2020-03-26T17:26:32Z
dc.date.issued2008
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this paper, we have compared the classifier algorithms including C4.5 decision tree, le artificial neural network (ANN), artificial immune recognition system (AIRS), and adaptive neuro-fuzzy inference system (ANFIS) in the diagnosis of obstructive sleep apnea syndrome (OSAS), which is an important disease that affects both the right and the left cardiac ventricle. The goal of this study was to find the best classifier model on the diagnosis of OSAS. The clinical features were obtained from Polysomnography device as a diagnostic tool for obstructive sleep apnea in patients clinically suspected of suffering this disease in this study. The clinical features are arousals index, apnea-hypopnea index (AHI), SaO(2) minimum value in stage of rapid eye movement, and percent sleep time in stage of SaO(2) intervals bigger than 89%. In our experiments, a total of 83 patients (58 with a positive OSAS (AHI > 5) and 25 with a negative OSAS such that normal subjects) were examined. The decision support systems can help to physicians in the diagnosing of any disorder or disease using clues obtained from signal or images taken from subject having any disorder. In order to compare the used classifier algorithms, the mean square error, classification accuracy, area under the receiver operating characteristics curve (AUC), and sensitivity and specificity analysis have been used. The obtained AUC values of C4.5 decision tree, ANN, AIRS, and ANFIS classifiers are 0.971, 0.96, 0.96, and 0.922, respectively. These results have shown that the best classifier system is C4.5 decision tree classifier on the diagnosis of obstructive sleep apnea syndrome.en_US
dc.identifier.doi10.1007/s10916-008-9129-9en_US
dc.identifier.endpage250en_US
dc.identifier.issn0148-5598en_US
dc.identifier.issn1573-689Xen_US
dc.identifier.issue3en_US
dc.identifier.pmid18444362en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage243en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s10916-008-9129-9
dc.identifier.urihttps://hdl.handle.net/20.500.12395/22267
dc.identifier.volume32en_US
dc.identifier.wosWOS:000254775900008en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofJOURNAL OF MEDICAL SYSTEMSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectobstructive sleep apnea syndrome (OSAS)en_US
dc.subjectC4.5 decision tree classifieren_US
dc.subjectartificial neural networken_US
dc.subjectartificial immune recognition systemen_US
dc.subjectadaptive neuro-fuzzy inference systemen_US
dc.subjectpolysomnographyen_US
dc.titleComparison of different classifier algorithms on the automated detection of obstructive sleep apnea syndromeen_US
dc.typeArticleen_US

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