Statistical Neural Networks in the Classification of Alcoholic Liver Disease and Nonalcoholic Fatty Liver Disease

This paper deals with the performance of statistical neural network in the classification of alcoholic liver disease (ALD) data and nonalcoholic fatty liver disease data (NAFLD). The study involved 73 individuals that were clinically diagnosed of alcoholic liver disease (ALD) and 80 individuals who were clinically diagnosed of nonalcoholic fatty liver disease (NAFLD). Four different neural network structure, multi-layer perceptron, radial basis function, probabilistic neural network and generalized regression neural network were applied to the data to determine the performance of statistical neural networks in the classification of liver disease data. The overall result indicates that the most suitable statistical neural network model for classifying ALD and NAFLD data is the probabilistic neural network (PNN) with a 95.7% classification performance and 67 correct classifications. Radial basis function network (RBF) and multilayer perceptron network (MLP) has the lowest classification accuracy with 55 classified samples each. The generalized regression neural network (GRNN) was the second-best network with 62 correct classifications. The computer simulation was carried out by using MATLAB 6.0 Neural Network Toolbox.