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Due to many possible causes involved with infertility, it is often difficult for medical doctors to diagnose the exact cause of the problem and to decide the correct therapy. A Bayesian network, in general, is widely accepted as an effective graphical model for analyzing biomedical data to determine associations among variables and to make probabilistic predictions of the expected values of hidden variables. This paper presents Bayesian network-based analysis of infertility patient data, which have been collected from the IVF clinic in a general hospital for two years. Through learning Bayesian networks from the clinical data, we identify the significant factors and their dependence relationships in determining the pregnancy of an infertility patient we classify the patient data into two classes (pregnant and not-pregnant) using the learned Bayesian network classifiers. From this medical data mining, we discovered the new domain knowledge that the age of female partner and stimulants like hCG, FSH, LH, Clomiphene, Parlodel and GnRH play the key role in pregnancy of an infertility patient. Through the experiments for investigating the prediction accuracy, Bayesian network classifiers showed the higher accuracy than non-Bayesian classifiers such as the decision tree and k-NN classifier.