Comparative Analysis of Back Propagation Neural Network and Probabilistic Neural Network for Diseases

Abstract

The diagnosis of diseases is a critical anddifficult job in medicine. An attempt to exploit knowledge and experience of several specialists and clinical screening data of patients composed in databases to assist the diagnosis procedure. Data Mining is the process of automating information discovery for finding relationships data to predict outcomes. In this paper, an efficient approach is compared for the intelligent diseases prediction based on Back Propagation Neural Network (BPNN) and Probabilistic Neural Network (PNN) techniques. This paper will compare the performance of BPNN and PNN based on their accuracy and execution time for predicting the diseases such as chronic kidney disease, hepatitis disease, heart disease and breast cancer disease