Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting: An Application of Machine Learning Methods.
Multiple embryo transfers in in vitro fertilization (IVF) treatment increase the number of successful pregnancies while elevating the risk of multiple gestations. IVF-associated multiple pregnancies exhibit significant financial, social, and medical implications. Clinicians need to decide the number of embryos to be transferred considering the tradeoff between successful outcomes and multiple pregnancies.



To predict implantation outcome of individual embryos in an IVF cycle with the aim of providing decision support on the number of embryos transferred.



Retrospective cohort study.



Electronic health records of one of the largest IVF clinics in Turkey. The study data set included 2453 embryos transferred at day 2 or day 3 after intracytoplasmic sperm injection (ICSI). Each embryo was represented with 18 clinical features and a class label, +1 or -1, indicating positive and negative implantation outcomes, respectively.



For each classifier tested, a model was developed using two-thirds of the data set, and prediction performance was evaluated on the remaining one-third of the samples using receiver operating characteristic (ROC) analysis. The training-testing procedure was repeated 10 times on randomly split (two-thirds to one-third) data. The relative predictive values of clinical input characteristics were assessed using information gain feature weighting and forward feature selection methods.



The naïve Bayes model provided 80.4% accuracy, 63.7% sensitivity, and 17.6% false alarm rate in embryo-based implantation prediction. Multiple embryo implantations were predicted at a 63.8% sensitivity level. Predictions using the proposed model resulted in higher accuracy compared with expert judgment alone (on average, 75.7% and 60.1%, respectively).



A machine learning-based decision support system would be useful in improving the success rates of IVF treatment.
Department of Computer Engineering, Okan University, Tuzla Kampusu, Tuzla, Istanbul, Turkey (AU)
Asli
A
Uyar
Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, Ontario, Canada (AB)
Ayse
A
Bener
Division of Reproduction and Early Development, Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, Leeds, UK (HNC)
H Nadir
HN
Ciray
D000328:Adult; D000465:Algorithms; D015331:Cohort Studies; D003661:Decision Support Techniques; D010064:Embryo Implantation; D004624:Embryo Transfer / Q000706:statistics & numerical data*; D005260:Female; D005307:Fertilization in Vitro / Q000706:statistics & numerical data*; D006801:Humans; D000069550:Machine Learning; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D011247:Pregnancy; D011272:Pregnancy, Multiple / Q000706:statistics & numerical data; D012189:Retrospective Studies; D020554:Sperm Injections, Intracytoplasmic; D014421:Turkey
embryo assessment; implantation prediction; in vitro fertilization; machine learning