Comparison of machine learning classification techniques to predict implantation success in an IVF treatment cycle

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dc.authorid0000-0002-5919-1986
dc.authorid0000-0002-7902-5803
dc.authorid0000-0003-3302-5004
dc.contributor.authorYiğit, Pakize
dc.contributor.authorBener, Abdulbari
dc.contributor.authorKarabulut, Seda
dc.date.accessioned2022-12-01T06:32:44Z
dc.date.available2022-12-01T06:32:44Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Temel Tıp Bilimleri Bölümü, Biyoistatistik ve Tıp Bilişimi Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, Uluslararası Tıp Fakültesi, Temel Tıp Bilimleri Bölümü, Histoloji ve Embriyoloji Ana Bilim Dalı
dc.description.abstractResearch question: Which machine learning model predicts the implantation outcome better in an IVF cycle? What is the importance of each variable in predicting the implantation outcome in an IVF cycle?Design: Retrospective cohort study comprising 939 transferred embryos between 2014 and 2018 in an IVF centre in Turkey with 17 selected features. The algorithms were Logistic Regression (LR), Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Neural Network (Nnet), Gradient Boost Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost) and Super Learner (SL). The results were evaluated with performance metrics (F1 score, specificity, accuracy and area under the receiver operating characteristic curve [AUROC]) with 10-fold cross-validation repeated ten times.Results: RF and SL models achieved the highest performance and showed F1 scores of 74% and 73%, specificity of 94%, an accuracy of 89%, and AUROC of 83%. In addition, the model identified the top features as maternal age, embryo transfer day, total gonadotrophin dose and oestradiol concentration.Conclusions: The present study revealed that machine learning algorithms successfully predicted implantation rates in an IVF attempt. In addition, maternal age is by far the most important predictor of IVF success when compared with other variables.
dc.description.sponsorshipIstanbul Medipol Universityen_US
dc.identifier.citationYiğit, P., Bener, A. ve Karabulut, S. (2022). Comparison of machine learning classification techniques to predict implantation success in an IVF treatment cycle. Reproductive BioMedicine Online, 42(5), 923-934. https://doi.org/10.1016/j.rbmo.2022.06.022
dc.identifier.doi10.1016/j.rbmo.2022.06.022
dc.identifier.endpage934
dc.identifier.issn1472-6483
dc.identifier.issn1472-6491
dc.identifier.issue5
dc.identifier.pmid36088224
dc.identifier.scopus2-s2.0-85137542128
dc.identifier.scopusqualityQ1
dc.identifier.startpage923
dc.identifier.urihttps://doi.org/10.1016/j.rbmo.2022.06.022
dc.identifier.urihttps://hdl.handle.net/20.500.12511/10051
dc.identifier.volume42
dc.identifier.wos000881110400013en_US
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorYiğit, Pakize
dc.institutionauthorBener, Abdulbari
dc.institutionauthorKarabulut, Seda
dc.language.isoen
dc.publisherElsevier Science Ltd
dc.relation.ispartofReproductive BioMedicine Onlineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectImplantation Prediction
dc.subjectIVF
dc.subjectMachine Learning
dc.subjectPrediction Models
dc.titleComparison of machine learning classification techniques to predict implantation success in an IVF treatment cycle
dc.typeArticle

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