A Bayesian network model for predicting pregnancy after in vitro fertilization
Computers in Biology and Medicine
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In in-vitro fertilization (IVF) treatment, blastocyst stage embryo transfers at day 5 result in higher pregnancy rates. However, there is a risk of transfer cancelation due to embryonic developmental failure. Clinicians need reliable models in predicting blastocyst development. In this study, we apply Bayesian networks in order to investigate cause-effect relationships of the variables of interest in embryo growth process and to predict blastocyst development. We have analyzed 7745 embryo records including embryo morphological characteristics and patient related data. Experimental results revealed that, Bayesian networks can predict blastocyst development with 63.5% true positive rate and 33.8% false positive rate.