An effective approach for breast cancer diagnosis based on routine blood analysis features

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dc.authorid0000-0002-3159-2497en_US
dc.contributor.authorYavuz, Erdem
dc.contributor.authorEyupoglu, Can
dc.date.accessioned2021-03-20T20:09:27Z
dc.date.available2021-03-20T20:09:27Z
dc.date.issued2020
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractBreast cancer is a widespread disease and one of the primary causes of cancer mortality among women all over the world. Computer-aided methods are used to assist medical doctors to make early diagnosis of the disease. The aim of this study is to build an effective prediction model for breast cancer diagnosis based on anthropometric data and parameters collected through routine blood analysis. The proposed approach innovatively exploits principal component analysis (PCA) technique cascaded by median filtering so as to transform original features into a form of containing less distractive noise not to cause overfitting. Since a generalized regression neural network (GRNN) model is adopted to classify patterns of the transformed features, the computational load imposed in the training of artificial neural network model is kept minimized thanks to the non-iterative nature of GRNN training. The proposed method has been devised and tested on the recent Breast Cancer Coimbra Dataset (BCCD) that contains 9 clinical features measured for each of 116 subjects. Outperforming all of the existing studies on BCCD, our method achieved a mean accuracy rate of 0.9773. Experimental results evidence that this study achieves the best prediction performance ever reported on this dataset. The fact that our proposed approach has accomplished such a boosted performance of breast cancer diagnosis based on routine blood analysis features offers a great potential to be used in a widespread manner to detect the disease in its inception phase. Graphical abstracten_US
dc.identifier.doi10.1007/s11517-020-02187-9en_US
dc.identifier.endpage1601en_US
dc.identifier.issn0140-0118
dc.identifier.issn1741-0444
dc.identifier.issue7en_US
dc.identifier.pmid32436139en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1583en_US
dc.identifier.urihttp://doi.org/10.1007/s11517-020-02187-9
dc.identifier.urihttps://hdl.handle.net/20.500.12885/426
dc.identifier.volume58en_US
dc.identifier.wosWOS:000535252000001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorYavuz, Erdem
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofMedical & Biological Engineering & Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBreast cancer diagnosisen_US
dc.subjectClassificationen_US
dc.subjectRoutine blood analysisen_US
dc.subjectMedian filteringen_US
dc.subjectPCAen_US
dc.subjectGRNNen_US
dc.titleAn effective approach for breast cancer diagnosis based on routine blood analysis featuresen_US
dc.typeArticleen_US

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