Estimating the chance of success in IVF treatment using a ranking algorithm

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dc.contributor.authorGüvenir, H. Altay
dc.contributor.authorMısırlı, Gizem
dc.contributor.authorDilbaz, Serdar
dc.contributor.authorÖzdeğirmenci, Özlem
dc.contributor.authorDemir, Berfu
dc.contributor.authorDilbaz, Berna
dc.date.accessioned2020-05-01T12:11:54Z
dc.date.available2020-05-01T12:11:54Z
dc.date.issued2015
dc.departmentDÜ, Tıp Fakültesi, Cerrahi Tıp Bilimleri Bölümüen_US
dc.descriptionGuvenir, Halil Altay/0000-0003-2589-316X;en_US
dc.descriptionWOS: 000361491900013en_US
dc.descriptionPubMed: 25894468en_US
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 Na < ve 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.description.sponsorshipBilkent UniversityIhsan Dogramaci Bilkent University; Etlik Zubeyde Hanim Women's Health Teaching and Research HospitalEtlik Zubeyde Hanim Gynecology Education & Research Hospitalen_US
dc.description.sponsorshipThis research was funded by Bilkent University and Etlik Zubeyde Hanim Women's Health Teaching and Research Hospital.en_US
dc.identifier.doi10.1007/s11517-015-1299-2en_US
dc.identifier.endpage920en_US
dc.identifier.issn0140-0118
dc.identifier.issn1741-0444
dc.identifier.issue9en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage911en_US
dc.identifier.urihttps://doi.org/10.1007/s11517-015-1299-2
dc.identifier.urihttps://hdl.handle.net/20.500.12684/6267
dc.identifier.volume53en_US
dc.identifier.wosWOS:000361491900013en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofMedical & Biological Engineering & Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEstimation of successen_US
dc.subjectRankingen_US
dc.subjectClassificationen_US
dc.subjectIn vitro fertilizationen_US
dc.subjectClinical decision support systemen_US
dc.titleEstimating the chance of success in IVF treatment using a ranking algorithmen_US
dc.typeArticleen_US

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