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dc.contributor.authorGüvenir, H. A.en_US
dc.contributor.authorMisirli, G.en_US
dc.contributor.authorDilbaz, S.en_US
dc.contributor.authorOzdegirmenci, O.en_US
dc.contributor.authorDemir, B.en_US
dc.contributor.authorDilbaz, B.en_US
dc.date.accessioned2016-02-08T09:55:27Z
dc.date.available2016-02-08T09:55:27Z
dc.date.issued2015en_US
dc.identifier.issn0140-0118
dc.identifier.urihttp://hdl.handle.net/11693/22092
dc.description.abstractIn medicine, estimating the chance of success for treatment is important in deciding whether to begin the treatment or not. This paper focuses on the domain of in vitro fertilization (IVF), where estimating the outcome of a treatment is very crucial in the decision to proceed with treatment for both the clinicians and the infertile couples. IVF treatment is a stressful and costly process. It is very stressful for couples who want to have a baby. If an initial evaluation indicates a low pregnancy rate, decision of the couple may change not to start the IVF treatment. The aim of this study is twofold, firstly, to develop a technique that can be used to estimate the chance of success for a couple who wants to have a baby and secondly, to determine the attributes and their particular values affecting the outcome in IVF treatment. We propose a new technique, called success estimation using a ranking algorithm (SERA), for estimating the success of a treatment using a ranking-based algorithm. The particular ranking algorithm used here is RIMARC. The performance of the new algorithm is compared with two well-known algorithms that assign class probabilities to query instances. The algorithms used in the comparison are Naïve Bayes Classifier and Random Forest. The comparison is done in terms of area under the ROC curve, accuracy and execution time, using tenfold stratified cross-validation. The results indicate that the proposed SERA algorithm has a potential to be used successfully to estimate the probability of success in medical treatment.en_US
dc.language.isoEnglishen_US
dc.source.titleMedical & Biological Engineering & Computingen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s11517-015-1299-2en_US
dc.subjectClassificationen_US
dc.subjectEstimation of successen_US
dc.subjectIn vitro fertilizationen_US
dc.subjectRankingen_US
dc.subjectArtificial intelligenceen_US
dc.subjectClassification (of information)en_US
dc.subjectDecision support systemsen_US
dc.subjectDecision treesen_US
dc.subjectArea under the ROC curveen_US
dc.subjectClass probabilitiesen_US
dc.subjectClinical decision support systemsen_US
dc.subjectIn-vitroen_US
dc.subjectMedical treatmenten_US
dc.subjectProbability of successen_US
dc.subjectRankingen_US
dc.subjectRanking algorithmen_US
dc.subjectAlgorithmsen_US
dc.subjectBayes theoremen_US
dc.subjectClassification algorithmen_US
dc.subjectFemaleen_US
dc.subjectFertilization in vitroen_US
dc.subjectHumanen_US
dc.subjectMajor clinical studyen_US
dc.subjectMeasurement accuracyen_US
dc.subjectOutcome assessmenten_US
dc.subjectPregnancy rateen_US
dc.subjectPriority journalen_US
dc.subjectProbabilityen_US
dc.subjectRandom foresten_US
dc.subjectReceiver operating characteristicen_US
dc.subjectValidation processen_US
dc.titleEstimating the chance of success in IVF treatment using a ranking algorithmen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineering
dc.citation.spage911en_US
dc.citation.epage920en_US
dc.citation.volumeNumber53en_US
dc.citation.issueNumber9en_US
dc.identifier.doi10.1007/s11517-015-1299-2en_US
dc.publisherSpringeren_US


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