The application of neural networks in predicting the outcome of in- vitro fertilization |
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Authors: | Kaufmann SJ; Eastaugh JL; Snowden S; Smye SW; Sharma V |
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Institution: | Assisted Conception Unit, St James's University Hospital, Leeds, UK. |
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Abstract: | Infertility affects one in six couples at some time in their lives, with
48% of these couples requiring assisted conception techniques in order to
achieve a pregnancy. Whilst the overall clinical pregnancy rate per embryo
transfer is 23%, this varies widely between clinics. The Human
Fertilisation and Embryology Authority has attempted to analyse the results
of all units, with weighting of different factors affecting assisted
conception, and the published data have invariably led to comparisons
between units. However, statistical models need to be developed to
eliminate bias for valid comparisons. Neural networks offer a novel
approach to pattern recognition. In some instances neural networks can
identify a wider range of associations than other statistical techniques
due in part to their ability to recognize highly non-linear associations.
It was hoped that a neural network approach may be able to predict success
for individual couples about to undergo in-vitro fertilization (IVF)
treatment. A neural network was constructed using the variables of age,
number of eggs recovered, number of embryos transferred and whether there
was embryo freezing. Overall the network managed to achieve an accuracy of
59%.
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