The comparisons of prognostic indexes using data mining techniques and Cox regression analysis in the breast cancer data

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dc.contributor.authorTure, Mevlut
dc.contributor.authorTokatli, Fusun
dc.contributor.authorOmurlu, Imran Kurt
dc.date.accessioned2024-06-12T10:59:10Z
dc.date.available2024-06-12T10:59:10Z
dc.date.issued2009
dc.departmentTrakya Üniversitesien_US
dc.description.abstractThe purpose of this study is to determine new prognostic indexes for the differentiation of subgroups of breast cancer patients with the techniques of decision tree algorithms (C&RT, CHAID, QUEST, ID3, C4.5 and C5.0) and Cox regression analysis for disease-free survival (DFS) in breast cancer patients. A retrospective analysis was performed in 381 breast cancer patients diagnosed. Age, menopausal status, age of menarche, family history of cancer, histologic tumor type, quadrant of tumor, tumor size, estrogen and progesterone receptor status, histologic and nuclear grading, axillary nodal status, pericapsular involvement of lymph nodes, lymphovascular and perineural invasion, adjuvant radiotherapy, chemotherapy and hormonal therapy were assessed. Based on these prognostic factors, new prognostic indexes for C&RT, CHAID, QUEST, ID3, C4.5 and C5.0 and Cox regression were obtained. Prognostic indexes showed a good degree of classification, which demonstrates that an improvement seems possible using standard risk factors. We obtained that C4.5 has a better performance than C&RT, CHAID, QUEST, ID3, C5.0 and Cox regression to determine risk groups using Random Survival Forests (RSF). (C) 2008 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2008.10.014
dc.identifier.endpage8254en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-60249097249en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage8247en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2008.10.014
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20349
dc.identifier.volume36en_US
dc.identifier.wosWOS:000264528600108en_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.subjectDecision Treeen_US
dc.subjectC&RTen_US
dc.subjectCHAIDen_US
dc.subjectQUESTen_US
dc.subjectID3en_US
dc.subjectC4.5en_US
dc.subjectC5.0en_US
dc.subjectCox Regressionen_US
dc.subjectKaplan-Meieren_US
dc.subjectBreast Canceren_US
dc.subjectDisease-Free Survivalen_US
dc.subjectRandom Survival Forestsen_US
dc.titleThe comparisons of prognostic indexes using data mining techniques and Cox regression analysis in the breast cancer dataen_US
dc.typeArticleen_US

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