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This paper presents an artificial neural network based approach for the diagnosis of hepatitis virus. The dataset used for this purpose is taken from the UCI machine learning database. Both supervised and unsupervised neural network models have been analyzed with different architectures, learning and activation functions. It is concluded that the supervised model performed better than the unsupervised one. The paper also compares the results of the previous studies on the diagnosis of hepatitis which use the same dataset.