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. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009.
CITATION STYLE
Uyar, A., Ciray, H. N., Bener, A., & Bahceci, M. (2009). 3P: Personalized pregnancy prediction in IVF treatment process. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (Vol. 1 LNICST, pp. 58–65). https://doi.org/10.1007/978-3-642-00413-1_7
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