This paper presented three disease diagnosis systems using pattern recognition based on genetic algorithm (GA) and neural networks. All systems dealt with feature selection and classification. GA chose subsets of features for the input of the classifier (neural network) and the accuracy of the classifier determined the percentage of effectiveness of each subset of features. The classifiers using in this paper were general regression neural network (GRNN), radial basis function (RBF) and radial basis network exact fit (RBEF). It uses breast cancer and hepatitis disease datasets taken from UCI machine learning database as medical dataset. The system performances were estimated by classification accuracy and they were compared with similar methods without feature selection.
Key words: Pattern recognition, genetic algorithm, neural networks, classification.
GA, Genetic algorithm; GRNN, general regression neural network; RBF,radial basis function; RBEF, radial basis network exact fit; RBFNs, radial basis function networks; PNNs, probabilistic neural networks; EBP, error-back-propagation; RBN, radial basis models.
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