An improved approach to medical data sets classification: artificial immune recognition system with fuzzy resource allocation mechanism

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dc.contributor.authorPolat, Kemal
dc.contributor.authorGuenes, Salih
dc.date.accessioned2020-03-26T17:16:59Z
dc.date.available2020-03-26T17:16:59Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe artificial immune recognition system (AIRS) has been shown to be an efficient approach to tackling a variety of problems such as machine learning benchmark problems and medical classification problems. In this study, the resource allocation mechanism of AIRS was replaced with a new one based on fuzzy logic. The new system, named Fuzzy-AIRS, was used as a classifier in the classification of three well-known medical data sets, the Wisconsin breast cancer data set (WBCD), the Pima Indians diabetes data set and the ECG arrhythmia data set. The performance of the Fuzzy-AIRS algorithm was tested for classification accuracy, sensitivity and specificity values, confusion matrix, computation time and receiver operating characteristic curves. Also, the AIRS and Fuzzy-AIRS algorithms were compared with respect to the amount of resources required in the execution of the algorithm. The highest classification accuracy obtained from applying the AIRS and Fuzzy-AIRS algorithms using 10-fold cross-validation was, respectively, 98.53% and 99.00% for classification of WBCD; 79.22% and 84.42% for classification of the Pima Indians diabetes data set; and 100% and 92.86% for classification of the ECG arrhythmia data set. Hence, these results show that Fuzzy-AIRS can be used as an effective classifier for medical problems.en_US
dc.identifier.doi10.1111/j.1468-0394.2007.00432.xen_US
dc.identifier.endpage270en_US
dc.identifier.issn0266-4720en_US
dc.identifier.issn1468-0394en_US
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage252en_US
dc.identifier.urihttps://dx.doi.org/10.1111/j.1468-0394.2007.00432.x
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21210
dc.identifier.volume24en_US
dc.identifier.wosWOS:000248961000004en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWILEYen_US
dc.relation.ispartofEXPERT SYSTEMSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectfuzzy resource allocationen_US
dc.subjectAIRSen_US
dc.subjectWisconsion breast cancer data seten_US
dc.subjectPima Indians diabetes data seten_US
dc.subjectECG arrhythmia data seten_US
dc.subjectROC curvesen_US
dc.subject10-fold cross-validationen_US
dc.titleAn improved approach to medical data sets classification: artificial immune recognition system with fuzzy resource allocation mechanismen_US
dc.typeArticleen_US

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