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Data mining approach to policy analysis in a health insurance domain

DC FieldValueLanguage
dc.contributor.author지선하-
dc.contributor.author채영문-
dc.date.accessioned2016-02-19T11:24:14Z-
dc.date.available2016-02-19T11:24:14Z-
dc.date.issued2001-
dc.identifier.issn1386-5056-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/143069-
dc.description.abstractThis study examined the characteristics of the knowledge discovery and data mining algorithms to demonstrate how they can be used to predict health outcomes and provide policy information for hypertension management using the Korea Medical Insurance Corporation database. Specifically, this study validated the predictive power of data mining algorithms by comparing the performance of logistic regression and two decision tree algorithms, CHIAD (Chi-squared Automatic Interaction Detection) and C5.0 (a variant of C4.5) using the test set of 4588 beneficiaries and the training set of 13,689 beneficiaries. Contrary to the previous study, the CHIAD algorithm performed better than the logistic regression in predicting hypertension, and C5.0 had the lowest predictive power. In addition, the CHIAD algorithm and the association rule also provided the segment-specific information for the risk factors and target group that may be used in a policy analysis for hypertension management.-
dc.description.statementOfResponsibilityopen-
dc.format.extent103~111-
dc.relation.isPartOfINTERNATIONAL JOURNAL OF MEDICAL INFORMATICS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.subject.MESHAlgorithms*-
dc.subject.MESHBiometry-
dc.subject.MESHChi-Square Distribution-
dc.subject.MESHDatabases, Factual-
dc.subject.MESHDecision Trees-
dc.subject.MESHFemale-
dc.subject.MESHHealth Promotion-
dc.subject.MESHHumans-
dc.subject.MESHHypertension/prevention & control*-
dc.subject.MESHInsurance, Health/statistics & numerical data*-
dc.subject.MESHKorea-
dc.subject.MESHLife Style-
dc.subject.MESHLogistic Models-
dc.subject.MESHMale-
dc.subject.MESHMiddle Aged-
dc.subject.MESHPredictive Value of Tests-
dc.subject.MESHRisk Factors-
dc.subject.MESHSensitivity and Specificity-
dc.titleData mining approach to policy analysis in a health insurance domain-
dc.typeArticle-
dc.contributor.collegeGraduate School of Public Health (보건대학원)-
dc.contributor.departmentGraduate School of Public Health (보건대학원)-
dc.contributor.googleauthorYoung Moon Chae-
dc.contributor.googleauthorSeung Hee Ho-
dc.contributor.googleauthorKyoung Won Cho-
dc.contributor.googleauthorDong Ha Lee-
dc.contributor.googleauthorSun Ha Ji-
dc.identifier.doi10.1016/S1386-5056(01)00154-X-
dc.admin.authorfalse-
dc.admin.mappingfalse-
dc.contributor.localIdA03965-
dc.contributor.localIdA04019-
dc.relation.journalcodeJ01129-
dc.identifier.eissn1872-8243-
dc.identifier.pmid11470613-
dc.identifier.urlhttp://www.sciencedirect.com/science/article/pii/S138650560100154X-
dc.subject.keywordKnowledge management-
dc.subject.keywordData mining-
dc.subject.keywordLogistic regression-
dc.subject.keywordHypertension-
dc.subject.keywordHealth insurance-
dc.contributor.alternativeNameJee, Sun Ha-
dc.contributor.alternativeNameChae, Young Moon-
dc.contributor.affiliatedAuthorJee, Sun Ha-
dc.contributor.affiliatedAuthorChae, Young Moon-
dc.rights.accessRightsnot free-
dc.citation.volume62-
dc.citation.number2-3-
dc.citation.startPage103-
dc.citation.endPage111-
dc.identifier.bibliographicCitationINTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, Vol.62(2-3) : 103-111, 2001-
Appears in Collections:
4. Graduate School of Public Health (보건대학원) > Graduate School of Public Health (보건대학원) > 1. Journal Papers

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