Samar Ashrfi Kakhki - ۱Department of Artificial Intelligence faculty engineering, IslamicAzad University, Mashhad Branch, Mashhad, Iran
Behnam Malekara - Department of Software faculty engineering, Islamic Azad University, Mashhad Branch, Mashhad, Iran
Saeed Rahati Quchani - Department of Artificial Intelligence faculty engineering, Islamic Azad University, Mashhad Branch, Mashhad, Iran
Nayereh Khadem - Mashhad University of Medical Sciences, Mashhad, Iran

In vitrofertilization (IVF) is one of assisted reproductive technique that enables infertile couples to achieve successful pregnancy. Accurateand early prediction of the outcome of the IVF is important for both patients and physicians. Given the uncertainty of the treatment, we propose an intelligent decision support system based on Bayesian Networks. We evaluate the effectiveness of four Bayesian network classifiers as potential tools for predictionthe success/failure rate of IVF using realworld database.Results show thatwe can use Bayesian networks as a new practical approach for predict the outcome of IVF treatment

Bayesian Networks, Prediction, Data mining, In vitro fertilization