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dc.contributor.authorKose, Guven
dc.contributor.authorSever, Hayri
dc.contributor.authorBal, Mert
dc.contributor.authorUstundag, Alp
dc.date.accessioned2019-12-20T07:23:20Z
dc.date.available2019-12-20T07:23:20Z
dc.date.issued2014
dc.identifier.issn1875-6891
dc.identifier.urihttps://doi.org/10.1080/18756891.2014.853929
dc.identifier.urihttp://hdl.handle.net/11655/21050
dc.description.abstractA medical diagnosis system (DRCAD), which consists of two sub-modules Bayesian and rule-based inference models, is presented in this study. Three types of tests are conducted to assess the performances of the models producing synthetic data based on the ALARM network. The results indicate that the linear combination of the aforementioned models leads to a 5% and a 30% improvement in medical diagnosis when compared to the Rule Based Method and the Bayesian Network Based Method, respectively.
dc.language.isoen
dc.publisherAtlantis Press
dc.relation.isversionof10.1080/18756891.2014.853929
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectComputer Science
dc.titleComparison Of Different Inference Algorithms For Medical Decision Making
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.relation.journalInternational Journal Of Computational Intelligence Systems
dc.contributor.departmentBilgi ve Belge Yönetimi
dc.identifier.volume7
dc.identifier.startpage29
dc.identifier.endpage44
dc.description.indexWoS
dc.description.indexScopus


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