Design of a hybrid system for the diabetes and heart diseases

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dc.contributor.authorKahramanli, Humar
dc.contributor.authorAllahverdi, Novruz
dc.date.accessioned2020-03-26T17:26:36Z
dc.date.available2020-03-26T17:26:36Z
dc.date.issued2008
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractData can be classified according to their properties. Classification is implemented by developing a model with existing records by using sample data. One of the aims of classification is to increase the reliability of the results obtained from the data. Fuzzy and crisp values are used together in medical data. Regarding to this, a new method is presented for classification of data of a medical database in this study. Also a hybrid neural network that includes artificial neural network (ANN) and fuzzy neural network (FNN) was developed. Two real-time problem data were investigated for determining the applicability of the proposed method. The data were obtained from the University of California at Irvine (UCI) machine learning repository. The datasets are Pima Indians diabetes and Cleveland heart disease. In order to evaluate the performance of the proposed method accuracy, sensitivity and specificity performance measures that are used commonly in medical classification studies were used. The classification accuracies of these datasets were obtained by k-fold cross-validation. The proposed method achieved accuracy values 84.24% and 86.8% for Pima Indians diabetes dataset and Cleveland heart disease dataset, respectively. It has been observed that these results are one of the best results compared with results obtained from related previous studies and reported in the UCI web sites. (C) 2007 Published by Elsevier Ltd.en_US
dc.identifier.doi10.1016/j.eswa.2007.06.004en_US
dc.identifier.endpage89en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issn1873-6793en_US
dc.identifier.issue01.02.2020en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage82en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2007.06.004
dc.identifier.urihttps://hdl.handle.net/20.500.12395/22300
dc.identifier.volume35en_US
dc.identifier.wosWOS:000257617100008en_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.subjectclassificationen_US
dc.subjectbackpropagationen_US
dc.subjectfuzzy neural networken_US
dc.subjectPima Indians diabetesen_US
dc.subjectCleveland heart diseaseen_US
dc.subjectk-fold cross-validationen_US
dc.titleDesign of a hybrid system for the diabetes and heart diseasesen_US
dc.typeArticleen_US

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