A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis

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dc.contributor.authorSahan, Seral
dc.contributor.authorPolat, Kemal
dc.contributor.authorKodaz, Halife
dc.contributor.authorGunes, Salih
dc.date.accessioned2020-03-26T17:16:56Z
dc.date.available2020-03-26T17:16:56Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. As the incidence of this disease has increased significantly in the recent years, machine learning applications to this problem have also took a great attention as well as medical consideration. This study aims at diagnosing breast cancer with a new hybrid machine learning method. By hybridizing a fuzzy-artificial immune system with k-nearest neighbour algorithm, a method was obtained to solve this diagnosis problem via classifying Wisconsin Breast Cancer Dataset (WBCD). This data set is a very commonly used data set in the literature relating the use of classification systems for breast cancer diagnosis and it was used in this study to compare the classification performance of our proposed method with regard to other studies. We obtained a classification accuracy of 99.14%, which is the highest one reached so far. The classification accuracy was obtained via 10-fold cross validation. This result is for WBCD but it states that this method can be used confidently for other breast cancer diagnosis problems, too. (c) 2006 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.compbiomed.2006.05.003en_US
dc.identifier.endpage423en_US
dc.identifier.issn0010-4825en_US
dc.identifier.issue3en_US
dc.identifier.pmid16904096en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage415en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.compbiomed.2006.05.003
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21178
dc.identifier.volume37en_US
dc.identifier.wosWOS:000244357600016en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofCOMPUTERS IN BIOLOGY AND MEDICINEen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectk-nearest neighbour classification systemen_US
dc.subjectartificial immune systemsen_US
dc.subjectfuzzy weightingen_US
dc.subjectbreast cancer diagnosisen_US
dc.subjectWisconsin Breast Cancer Diagnosis Dataen_US
dc.subjectk-fold cross validationen_US
dc.titleA new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosisen_US
dc.typeArticleen_US

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