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

RESEARCH 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), Naïve 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.