ON THE DEVELOPMENT OF NEURAL NETWORK MODELS USING DATA MINING TOOLS

Abstract

This paper consists of two models, based on the famous Artificial Neural Network (ANN) Models: Multi Layered Perceptron (MLP) model and Cascade Correlation network model developed and compared in their ability to judge the accuracy in results obtained by the diagnosis of the disease in patients. The significance of disease diagnosis by Artificial Neural techniques is not at all obscure these days. The increasing demand of Artificial Neural Networks in the field of medicine has proved to show a significantly better performance in medical decision making. These networks are used to diagnose a wide variety of diseases based on the inputs to the model. The diagnoses are made on specific models with information taken from a large number of patients as compared to a single one. These models do not depend on the assumption made by correlation of different variables. One of the proposed techniques involves the training of the Multi Layered Perceptron (MLP)to recognize a pattern for the diagnosis and prediction of the diseases. For this purpose, we have used various featured inputs on the basis of patients unique like age, sex, marital status, signs and symptoms. On the whole, the Multi Layered Perceptron (MLP) model has proved to diagnose the diseases of multiple patient outcomes more accurately than Cascade Correlation network model for the validated data. Moreover the results have also significantly demonstrated the suitability of the neural network models for specifying the disease the patient possesses.