Using Kaplan-Meier analysis together with decision tree methods (C&RT, CHAID, QUEST, C4.5 and ID3) in determining recurrence-free survival of breast cancer patients

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dc.contributor.authorTure, Mevlut
dc.contributor.authorTokatli, Fusun
dc.contributor.authorKurt, Imran
dc.date.accessioned2024-06-12T10:59:10Z
dc.date.available2024-06-12T10:59:10Z
dc.date.issued2009
dc.departmentTrakya Üniversitesien_US
dc.description.abstractCurrent evidence supports a clear association between clinical and pathologic factors and recurrence-free survival (RFS) in breast cancer patients. The Cox regression model is the most common tool for investigating simultaneously the influence of several factors oil the survival time of patients. But it gives no estimate of the degree of separation of the different Subgroups. We propose to analyze different decision tree methods (C&RT, CHAID. QUEST, C4.5 and ID3) and use them additionally to the well-known Kaplan-Meier estimates to investigate the predictive power of these methods. Five hundred patients were included to the study. Two hundred and seventy-nine of them had complete data for prognostic factors and median follow-up is about 40.5 months. First, decision tree methods were analyzed for prognostic factors. Then, according to multidimensional scaling method C4.5 (error rate 0.2258 for training set and 0.3259 for cross-validation) performed slightly better than other methods in predicting risk factors for recurrence. Tumor size, age of menarche, hormonal therapy. histological grade and axillary nodal Status arc found that in important risk factors for the recurrence. Eight terminal nodes were found and stratified by Kaplan-Meier survival curves. Larger tumor size (>= 4.4 cm) and receiving no hormonal therapy in a small subgroup of patients were associated with worse prognosis. The five-year RFS is 71.3% in the whole patient population. The sensitivity, specificity and predictive rates calculated by C4.5 method were found 43.8%, 91% and 77.4% respectively. In this study, C4.5 showed a better degree of separation. As a result, we recommend 10 use decision tree methods together with Kaplan-Meier analysis to determine risk factors and effect of this factors oil survival. (C) 2008 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2007.12.002
dc.identifier.endpage2026en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-56349112446en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2017en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2007.12.002
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20348
dc.identifier.volume36en_US
dc.identifier.wosWOS:000262178000102en_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.subjectC4.5en_US
dc.subjectID3en_US
dc.subjectKaplan-Meieren_US
dc.subjectBreast Cancersen_US
dc.subjectRecurrence-Free Survivalen_US
dc.subjectAssociationen_US
dc.subjectExpressionen_US
dc.titleUsing Kaplan-Meier analysis together with decision tree methods (C&RT, CHAID, QUEST, C4.5 and ID3) in determining recurrence-free survival of breast cancer patientsen_US
dc.typeArticleen_US

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