A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis

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dc.contributor.authorOzsen, Seral
dc.contributor.authorKara, Sadik
dc.contributor.authorLatifoglu, Fatma
dc.contributor.authorGunes, Salih
dc.date.accessioned2020-03-26T17:16:57Z
dc.date.available2020-03-26T17:16:57Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractBecause of its self-regulating nature, immune system has been an inspiration source for usually unsupervised learning methods in classification applications of Artificial Immune Systems (AIS). But classification with supervision can bring some advantages to AIS like other classification systems. Indeed, there have been some studies, which have obtained reasonable results and include supervision in this branch of AIS. In this study, we have proposed a new supervised AIS named as Supervised Affinity Maturation Algorithm (SAMA) and have presented its performance results through applying it to diagnose atherosclerosis using carotid artery Doppler signals as a real-world medical classification problem. We have employed the maximum envelope of the carotid artery Doppler sonograms derived from Autoregressive (AR) method as an input of proposed classification system and reached a maximum average classification accuracy of 98.93% with 10-fold cross-validation method used in training-test portioning. To evaluate this result, comparison was done with Artificial Neural Networks and Decision Trees. Our system was found to be comparable with those systems, which are used effectively in literature with respect to classification accuracy and classification time. Effects of system's parameters were also analyzed in performance evaluation applications. With this study and other possible contributions to AIS, classification algorithms with effective performances can be developed and potential of AIS in classification can be further revealed. (C) 2007 Elsevier Ireland Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.cmpb.2007.09.002en_US
dc.identifier.endpage255en_US
dc.identifier.issn0169-2607en_US
dc.identifier.issn1872-7565en_US
dc.identifier.issue3en_US
dc.identifier.pmid17976855en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage246en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.cmpb.2007.09.002
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21185
dc.identifier.volume88en_US
dc.identifier.wosWOS:000251683400007en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherELSEVIER IRELAND LTDen_US
dc.relation.ispartofCOMPUTER METHODS AND PROGRAMS IN BIOMEDICINEen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectartificial immune systemsen_US
dc.subjectsupervised learningen_US
dc.subjectatherosclerosisen_US
dc.subjectcarotid artery Doppler signalsen_US
dc.titleA new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosisen_US
dc.typeArticleen_US

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