Electronic Healthcare. First International Conference, eHealth 2008, London, UK, September 8-9, 2008. Revised Selected Papers

Research Article

3P: Personalized Pregnancy Prediction in IVF Treatment Process

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  • @INPROCEEDINGS{10.1007/978-3-642-00413-1_7,
        author={Asli Uyar and H. Ciray and Ayse Bener and Mustafa Bahceci},
        title={3P: Personalized Pregnancy Prediction in IVF Treatment Process},
        proceedings={Electronic Healthcare. First International Conference, eHealth 2008, London, UK, September 8-9, 2008. Revised Selected Papers},
        proceedings_a={E-HEALTH},
        year={2012},
        month={5},
        keywords={In-vitro fertilization Embryo implantation prediction Classification Support vector machines},
        doi={10.1007/978-3-642-00413-1_7}
    }
    
  • Asli Uyar
    H. Ciray
    Ayse Bener
    Mustafa Bahceci
    Year: 2012
    3P: Personalized Pregnancy Prediction in IVF Treatment Process
    E-HEALTH
    Springer
    DOI: 10.1007/978-3-642-00413-1_7
Asli Uyar1,*, H. Ciray2,*, Ayse Bener1,*, Mustafa Bahceci2,*
  • 1: Boğaziçi University
  • 2: Bahceci Women Health Care Centre
*Contact email: asli.uyar@boun.edu.tr, nadirc@superonline.com, bener@boun.edu.tr, mbahceci@superonline.com

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.