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

Journal: Fertility and sterility
Published Date: Feb 1, 2019

Abstract

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), 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.

Authors

  • Celine Blank
    Department of Obstetrics and Gynecology, Catharina Hospital, Eindhoven, the Netherlands; Department of Electrical Engineering (Signal Processing Systems), Eindhoven Technical University, Eindhoven, the Netherlands; Department of Reproductive Medicine, Ghent University Hospital, Ghent, Belgium. Electronic address: celineblank@icloud.com.
  • Rogier Rudolf Wildeboer
    Department of Electrical Engineering (Signal Processing Systems), Eindhoven Technical University, Eindhoven, the Netherlands.
  • Ilse DeCroo
    Department of Reproductive Medicine, Ghent University Hospital, Ghent, Belgium.
  • Kelly Tilleman
    Department of Reproductive Medicine, Ghent University Hospital, Ghent, Belgium.
  • Basiel Weyers
    Department of Reproductive Medicine, Ghent University Hospital, Ghent, Belgium.
  • Petra De Sutter
  • Massimo Mischi
    Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
  • Benedictus Christiaan Schoot
    Department of Obstetrics and Gynecology, Catharina Hospital, Eindhoven, the Netherlands; Department of Reproductive Medicine, Ghent University Hospital, Ghent, Belgium.

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