A hybrid approach to medical decision support systems: Combining feature selection, fuzzy weighted pre-processing and AIRS

Basit Öğe Kaydı
dc.contributor.authorPolat, Kemal
dc.contributor.authorGunes, Salih
dc.date.accessioned2020-03-26T17:16:55Z
dc.date.available2020-03-26T17:16:55Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis paper presents a hybrid approach based on feature selection, fuzzy weighted preprocessing and artificial immune recognition system (AIRS) to medical decision support systems. we have used the heart disease and hepatitis disease datasets taken from UCI machine learning database as medical dataset. Artificial immune recognition system has shown an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer diabetes and liver disorders classification. The proposed approach consists of three stages. In the first stage, the dimensions of heart disease and hepatitis disease datasets are reduced to 9 from 13 and 19 in the feature selection (FS) sub-program by means of C4.5 decision tree algorithm (CBA program), respectively In the second stage, heart disease and hepatitis disease datasets are normalized in the range of [0,1] and are weighted via fuzzy weighted pre-processing. In the third stage, weighted input values obtained from fuzzy weighted pre-processing are classified using AIRS classifier system. The obtained classification accuracies of our system are 9239% and 81.82% using 50-50% training-test split for heart disease and hepatitis disease datasets, respectively with these results, the proposed method can be used in medical decision support systems. (c) 2007 Elsevier Ireland Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.cmpb.2007.07.013en_US
dc.identifier.endpage174en_US
dc.identifier.issn0169-2607en_US
dc.identifier.issue2en_US
dc.identifier.pmid17884235en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage164en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.cmpb.2007.07.013
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21171
dc.identifier.volume88en_US
dc.identifier.wosWOS:000250840300008en_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.subjectAIRSen_US
dc.subjectfuzzy weighted pre-processingen_US
dc.subjectfeature selectionen_US
dc.subjectheart diseaseen_US
dc.subjecthepatitis diseaseen_US
dc.subjectmedical decision-makingen_US
dc.titleA hybrid approach to medical decision support systems: Combining feature selection, fuzzy weighted pre-processing and AIRSen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu