A hybrid breast cancer detection system via neural network and feature selection based on SBS, SFS and PCA

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dc.contributor.authorUzer, Mustafa Serter
dc.contributor.authorInan, Onur
dc.contributor.authorYilmaz, Nihat
dc.date.accessioned2020-03-26T18:40:59Z
dc.date.available2020-03-26T18:40:59Z
dc.date.issued2013
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractTwo hybrid feature selection methods (SFSP and SBSP) which are composed by combining the sequential forward selection and the sequential backward selection together with the principal component analysis developed by utilizing quadratic discriminant analysis classification algorithmic criteria so as to utilize in the diagnosis of breast cancer fast and effectively are presented in this study. The tenfold cross-validation method has been applied in the algorithm, which is utilized as criteria during the selection of the features. The dimension of the feature space for input has been decreased from 9 to 4 thanks to the selection of these two hybrid features. The Artificial Neural Networks have been used as classifier. The cross-validation method has been preferred also in the phase of this classification as in the case of the selection of the feature in order to increase the reliability of the result. The Wisconsin Breast Cancer Database obtained from the UCI has been utilized so as to determine the correctness of the system suggested. The values of the average correctness of the classification obtained by utilizing a tenfold cross-validation of the two hybrid systems developed earlier are found, respectively, as follows: for SFSP + NN, 97.57 % and for SBSP + NN, 98.57 %. SBSP + NN system has been observed that, among the studies carried out by implementing the cross-validation method for the breast cancer, the result appears to be very promising. The acquired results have revealed that this hybrid system applied by means of reducing dimension is an utilizable system in order to diagnose the diseases faster and more successfully.en_US
dc.description.sponsorshipSelcuk UniversitySelcuk Universityen_US
dc.description.sponsorshipThe authors are grateful to Selcuk University Scientific Research Projects Coordinatorship for support of the manuscript.en_US
dc.identifier.doi10.1007/s00521-012-0982-6en_US
dc.identifier.endpage728en_US
dc.identifier.issn0941-0643en_US
dc.identifier.issue03.04.2020en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage719en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-012-0982-6
dc.identifier.urihttps://hdl.handle.net/20.500.12395/29151
dc.identifier.volume23en_US
dc.identifier.wosWOS:000324794200017en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofNEURAL COMPUTING & APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectFeature selectionen_US
dc.subjectNeural networken_US
dc.subjectBreast cancer diagnosisen_US
dc.subjectSFSen_US
dc.subjectSBSen_US
dc.subjectPCAen_US
dc.titleA hybrid breast cancer detection system via neural network and feature selection based on SBS, SFS and PCAen_US
dc.typeArticleen_US

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