A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis

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dc.contributor.authorPolat, Kemal
dc.contributor.authorSahan, Seral
dc.contributor.authorGuenes, Salih
dc.date.accessioned2020-03-26T17:16:57Z
dc.date.available2020-03-26T17:16:57Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractProper interpretation of the thyroid gland functional data is an important issue in the diagnosis of thyroid disease. The primary role of the thyroid gland is to help regulation of the body's metabolism. Thyroid hormone produced by the thyroid gland provides this. Production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism) defines the type of thyroid disease. Artificial immune systems (AISs) is a new but effective branch of artificial intelligence. Among the systems proposed in this field so far, artificial immune recognition system (AIRS), which was proposed by A. Watkins, has shown an effective and intriguing performance on the problems it was applied. This study aims at diagnosing thyroid disease with a new hybrid machine learning method including this classification system. By hybridizing AIRS with a developed Fuzzy weighted pre-processing, a method is obtained to solve this diagnosis problem via classifying. The robustness of this method with regard to sampling variations is examined using a cross-validation method. We used thyroid disease dataset which is taken from UCI machine learning respiratory. We obtained a classification accuracy of 85%, which is the highest one reached so far. The classification accuracy was obtained via a 10-fold cross-validation. (C) 2006 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2006.02.007en_US
dc.identifier.endpage1147en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issn1873-6793en_US
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1141en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2006.02.007
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21187
dc.identifier.volume32en_US
dc.identifier.wosWOS:000243797800018en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONSen_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.subjectartificial immune recognition system (AIRS)en_US
dc.subjectfuzzy weighted pre-processingen_US
dc.subjectthyroid disease diagnosisen_US
dc.titleA novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosisen_US
dc.typeArticleen_US

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