Abstract
The accessibility of large amounts of medicinal data in clinics and hospitals pointers to the focus on reliable information analysis software to exploit useful information. Many tools tried to diagnosis hepatitis disease but still there is a deficiency of analyzing the biological data of Hepatitis illness in the world, where millions of people are killed in the world by this disease. This research aims at investigating the neural network algorithm for hepatitis disease. The data mining processes applied on the UCI dataset. Our investigation model examined different types of neural network algorithms (Quick, Multiple, Dynamic and RBFN) with different factors such as data size, learning cycle, and processing time to achieve the diagnosis accuracy and estimated error. The Multiple neural networks proved the best performance compared with Quick, Dynamic, and RBF neural network algorithms.