Abstract
We present an intelligent learning system for improving pregnancy success rate of IVF treatment. Our proposed model uses an SVM based classification system for training a model from past data and making predictions on implantation outcome of new embryos. This study employs an embryo-centered approach. Each embryo is represented with a data feature vector including 17 features related to patient characteristics, clinical diagnosis, treatment method and embryo morphological parameters. Our experimental results demonstrate a prediction accuracy of 82.7%. We have obtained the IVF dataset from Bahceci Women Health, Care Centre, in Istanbul, Turkey.
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© 2009 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Uyar, A., Ciray, H.N., Bener, A., Bahceci, M. (2009). 3P: Personalized Pregnancy Prediction in IVF Treatment Process. In: Weerasinghe, D. (eds) Electronic Healthcare. eHealth 2008. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 0001. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00413-1_7
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DOI: https://doi.org/10.1007/978-3-642-00413-1_7
Publisher Name: Springer, Berlin, Heidelberg
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