Medical Diagnosis on Pima Indian Diabetes Using General Regression Neural Networks

The performance of recently developed neural network structure, general regression neural network (GRNN), is examined on the medical data. Pima Indian Dabetes (PID) data set is chosen to study on that had been examined by more complex neural network structures in the past. The results of early studies and of the GRNN structure presented in this paper is compared. Close classification accuracy to the reference work using ARTMAP-IC structured model, which is the best result obtained since now, is achieved by using GRNN, which has a simpler structure. The performance of the standard multilayer perceptron (MLP) and radial basis function (RBF) feed forward neural networks are also examined for the comparison as they are the most general and commonly used neural network structures. The performance of the MLP was tested for different types of backpropagation training algorithms.