Diagnosis Of Diabetes Mellitus By Using Artificial Neural Networks

Abstract

Diabetes mellitus is a chronic disease which occurs when the pancreas does not produce sufficient insulin, or when thebody cannot effectively use the insulin it produces. It is an important and relatively common medical condition and isa risk factor for many other medical conditions like stroke,renal failure, blindness, kidney disease and coronary artery disease. Physicians have to elicit a comprehensive medical history and thorough physical examination before diabetes mellituscan be suspected. In this study a lot of data has been collected on the diseases diagnosis The use of neural networks for diabetic Diagnosis has also attracted the interest of the medical informatics community because of their ability to model the nonlinear nature ,the goal of this study is to design a novel approach for diagnosing diabetes using Artificial Neural Networks. The study aims also identify the best ANNs type which is more suitable for diagnoses diabetes .so three ANNs was designed which are feed forwardback propagation ,Recurrent and Elman network to designed which one has the best performance . Using MATLAB BPF was designed and trained in the BB NN , RNN, and elman network ,first one hidden layer between input and output layer with changed about (3,4,5,6,7,8,9) neuron in hidden layer . type of activation function which chooses firstly is log sigmoid and tan sigmoid function. The result obtained that amount the three neural network. 1- theelman network with 5 neuron in the hidden layer with TAN sigmoid activation function has performance 0.00083289 2- For the BPNN with 7 neuron in the hidden layer with TAN sigmoid activation function has performance 0.00011178 that is best performance network in this study. 3- For the RNN with 8 neuron in the hidden layer with LOG sigmoid activation function has performance 0.00019097