Urinary System Diseases Diagnosis Using Artificial Neural Networks

The goal of this paper is to evaluate artificial neural network in urinary diseases diagnosis. Artificial neural networks are widely used in medical problems. Artificial neural networks are used to disease diagnosis. Feed-forward back propagation neural network is used as a classifier to distinguish between infected or non-infected with two types of urinary disease. Inflammation of urinary bladder and nephritis of renal pelvis origin are diagnosis by artificial neural network. The results of applying the artificial neural networks methodology to diagnosis based upon selected symptoms show abilities of the network to learn the patterns corresponding to symptoms of the person. In this study, the data were obtained from UCI Machine Learning Repository in order to diagnosed diseases. The data is separated into inputs and targets. The symptoms will act as the inputs to the neural network. The targets for the neural network will be identified with 1's as infected and will be identified with 0's as non-infected. In all cases, the percent correctly classified in the simulation sample by the feed-forward back propagation network is 99 percent. The results show that the proposed diagnosis neural network could be useful for identifying the infected person.