Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective

Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective
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), based on data from a large cohort including in vitro fertilization (IVF) patients treated with the use of single-embryo transfer (SET) of blastocyst-stage embryos.
Source: fertstert.org