Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree

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dc.contributor.buuauthorYılmaz, Ersen
dc.contributor.buuauthorKılıkçıer, Çaǧlar
dc.contributor.departmentUludağ Üniversitesi/Mühendislik Fakültesi/Elektrik ve Elektronik Mühendisliği Bölümü.tr_TR
dc.contributor.orcid0000-0001-7933-1643tr_TR
dc.contributor.researcheridG-3554-2013tr_TR
dc.contributor.researcheridAAH-3031-2021tr_TR
dc.contributor.scopusid56965095300tr_TR
dc.contributor.scopusid55946623600tr_TR
dc.date.accessioned2022-06-21T11:33:28Z
dc.date.available2022-06-21T11:33:28Z
dc.date.issued2013
dc.description.abstractWe use least squares support vector machine (LS-SVM) utilizing a binary decision tree for classification of cardiotocogram to determine the fetal state. The parameters of LS-SVM are optimized by particle swarm optimization. The robustness of the method is examined by running 10-fold cross-validation. The performance of the method is evaluated in terms of overall classification accuracy. Additionally, receiver operation characteristic analysis and cobweb representation are presented in order to analyze and visualize the performance of the method. Experimental results demonstrate that the proposed method achieves a remarkable classification accuracy rate of 91.62%.en_US
dc.identifier.citationYilmaz, E. ve Kılıkçıer, Ç. (2013). "Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree". Computational and Mathematical Methods in Medicine, 2013.en_US
dc.identifier.issn1748-670X
dc.identifier.issn1748-6718
dc.identifier.pubmed24288574tr_TR
dc.identifier.scopus2-s2.0-84888869975tr_TR
dc.identifier.urihttps://doi.org/10.1155/2013/487179
dc.identifier.urihttps://www.hindawi.com/journals/cmmm/2013/487179/
dc.identifier.urihttp://hdl.handle.net/11452/27335
dc.identifier.volume2013tr_TR
dc.identifier.wos000326751100001tr_TR
dc.indexed.pubmedPubMeden_US
dc.indexed.scopusScopusen_US
dc.indexed.wosSCIEen_US
dc.language.isoenen_US
dc.publisherHindawien_US
dc.relation.journalComputational and Mathematical Methods in Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergitr_TR
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMathematical & computational biologyen_US
dc.subjectHeart-rateen_US
dc.subjectClassificationen_US
dc.subjectPerformanceen_US
dc.subjectSystemen_US
dc.subjectRisken_US
dc.subjectBinary treesen_US
dc.subjectDecision treesen_US
dc.subjectParticle swarm optimization (PSO)en_US
dc.subject10-fold cross-validationen_US
dc.subjectBinary decision treesen_US
dc.subjectCardiotocogramen_US
dc.subjectClassification accuracyen_US
dc.subjectLeast squares support vector machinesen_US
dc.subjectOperation characteristicen_US
dc.subjectSupport vector machinesen_US
dc.subject.emtreeArticleen_US
dc.subject.emtreeCardiotocographen_US
dc.subject.emtreeCardiotocographyen_US
dc.subject.emtreeClassification algorithmen_US
dc.subject.emtreeClinical evaluationen_US
dc.subject.emtreeDecision treeen_US
dc.subject.emtreeDiagnostic accuracyen_US
dc.subject.emtreeFetusen_US
dc.subject.emtreeFetus developmenten_US
dc.subject.emtreeHumanen_US
dc.subject.emtreeImage analysisen_US
dc.subject.emtreeIntelligenceen_US
dc.subject.emtreeLearning algorithmen_US
dc.subject.emtreeLeast square support vector machineen_US
dc.subject.emtreeMachine learningen_US
dc.subject.emtreeNonhumanen_US
dc.subject.emtreeParametersen_US
dc.subject.emtreeParticle swarm optimizationen_US
dc.subject.emtreeProcess optimizationen_US
dc.subject.emtreeReceiver operating characteristicen_US
dc.subject.emtreeSupport vector machineen_US
dc.subject.emtreeArtificial intelligenceen_US
dc.subject.emtreeCardiotocographyen_US
dc.subject.emtreeDecision support systemen_US
dc.subject.emtreeDecision treeten_US
dc.subject.emtreeEvaluation studyen_US
dc.subject.emtreeFemaleen_US
dc.subject.emtreePregnancyen_US
dc.subject.emtreeRegression analysisen_US
dc.subject.emtreeStatistics and numerical dataen_US
dc.subject.emtreeSupport vector machineen_US
dc.subject.emtreeValidation studyen_US
dc.subject.emtreeStatisticsen_US
dc.subject.meshArtificial intelligenceen_US
dc.subject.meshCardiotocographyen_US
dc.subject.meshDecision support systems, clinicalen_US
dc.subject.meshDecision treesen_US
dc.subject.meshFemaleen_US
dc.subject.meshHumansen_US
dc.subject.meshLeast-squares analysisen_US
dc.subject.meshPregnancyen_US
dc.subject.meshROC curveen_US
dc.subject.meshSupport vector machinesen_US
dc.subject.scopusCardiotocography; Fetal Heart Rate; Pregnancyen_US
dc.subject.wosMathematical & Computational Biologyen_US
dc.titleDetermination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision treeen_US
dc.typeArticle
dc.wos.quartileQ3en_US

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