Medical Diagnosis using Back propagation Algorithm

Abstract

Abstract — Artificial Intelligence has always lent a helping hand to the practitioners of medicine for improving medical diagnosis and treatment then, paradigm of artificial neural networks is shortly introduced and the main problems of medical data base and the basic approaches for training and testing a network by medical data are described. A lot of Applications tried to help human experts, offering a solution. This paper describes a optimal feed forward Back propagation algorithm. Feedforward back propagation neural network is used as a classifier to distinguish between infected or non-infected person in both cases. However, Traditional Back propagation algorithm has many shortcomings. Learning often takes long time to converge, and it may fall into local minima. One of the possible remedies to escape from local minima is by using a very small learning rate, which slows down the learning process. The back propagation algorithm presented in this paper used for training depends on a multilayer neural network with a very small learning rate, especially when using a large training set size. It can be applied in a generic manner for any network size that uses a back propagation algorithm and achieved the best performance with the minimum epoch (training iterations) and training time.