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 …

Background In vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) are two
important subsets of the assisted reproductive techniques, used for the treatment of infertility …

Background. Multiple embryo transfers in in vitro fertilization (IVF) treatment increase the
number of successful pregnancies while elevating the risk of multiple gestations. IVF …

Background In vitro fertilization (IVF) is a useful assisted reproductive technology to achieve
pregnancy in infertile couples. However, it is very important to optimize the success rate after …

Infertility affects one out of seven couples around the world. Therefore, the best possible
management of the in vitro fertilization (IVF) treatment and patient advice is crucial for both …

Introduction Assisted reproductive technology has been proposed for women with infertility.
Moreover, in vitro fertilization (IVF) cycles are increasing. Factors contributing to successful …

Background The low live birth rate and difficult decision-making of the in vitro fertilization
(IVF) treatment regimen bring great trouble to patients and clinicians. Based on the …

Purpose To assess whether machine learning methods provide advantage over classic
statistical modeling for the prediction of IVF outcomes. Methods The study population …

Infertility is a significant health problem and assisted reproductive technologies to treat
infertility. Despite all efforts, the success rate of these methods is still low. Also, each of these …

Objective To develop a random forest model (RFM) to predict implantation potential of a
transferred embryo and compare it with a multivariate logistic regression model (MvLRM) …