Comparison of logistic regression model and classification tree: An application to postpartum depression data

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dc.contributor.authorCamdeviren, Handan Ankarali
dc.contributor.authorYazici, Ayse Canan
dc.contributor.authorAkkus, Zeki
dc.contributor.authorBugdayci, Resul
dc.contributor.authorSungur, Mehmet Ali
dc.date.accessioned2024-04-24T16:11:24Z
dc.date.available2024-04-24T16:11:24Z
dc.date.issued2007
dc.departmentDicle Üniversitesien_US
dc.description.abstractIn this study, it is aimed that comparing logistic regression model with classification tree method in determining social-demographic risk factors which have effected depression status of 1447 women in separate postpartum periods. In determination of risk factors, data obtained from prevalence study of postpartum depression were used. Cut-off value of postpartum depression scores that calculated was taken as 13. Social and demographic risk factors were brought up by helping of the classification tree and logistic regression model. According to optimal classification tree total of six risk factors were determined, but in logistic regression model 3 of their effect, were found significantly. In addition, during the relations among risk factors in tree structure were being evaluated, in logistic regression model corrected main effects belong to risk factors were calculated. In spite of, classification success of maximal tree was found better than both optimal tree and logistic regression model, it is seen that using this tree structure in practice is very difficult. But we say that the logistic regression model and optimal tree had the lower sensitivity, possibly due to the fact that numbers of the individuals in both two groups were not equal and clinical risk factors were not considered in this study. Classification tree method gives more information with detail on diagnosis by evaluating a lot of risk factors together than logistic regression model. But making correct selection through constructed tree structures is very important to increase the success of results and to reach information which can provide appropriate explanations. (C) 2006 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2006.02.022
dc.identifier.endpage994en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-33751428610
dc.identifier.scopusqualityQ1
dc.identifier.startpage987en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2006.02.022
dc.identifier.urihttps://hdl.handle.net/11468/15379
dc.identifier.volume32en_US
dc.identifier.wosWOS:000243797800003
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassification And Regression Treesen_US
dc.subjectLogistic Regression Modelen_US
dc.subjectCross-Validationen_US
dc.subjectPostpartum Depressionen_US
dc.subjectDiagnostic Modelsen_US
dc.titleComparison of logistic regression model and classification tree: An application to postpartum depression dataen_US
dc.titleComparison of logistic regression model and classification tree: An application to postpartum depression data
dc.typeArticleen_US

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