Quantum-Behaved Particle Swarm Optimization Based Radial Basis Function Network for Classification of Clinical Datasets

Abstract

In this article, a classification framework that uses quantum-behaved particle swarm optimization neural network (QPSONN) classifiers for diagnosing a disease is discussed. The neural network used for classification is radial basis function neural network (RBFNN). For training the RBFNN K-means clustering algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm has been used. The K-means clustering algorithm is used to find the optimal number of clusters which determines the number of neurons in the hidden layer. The cluster approximation error is used to find the optimal clusters. The weights between the hidden and the output layer is determined using QPSO algorithm based on the mean squared error (MSE). The performance of the developed classifier model has been tested with five clinical datasets, namely Pima Indian Diabetes, Hepatitis, Bupa Liver Disease, Wisconsin Breast Cancer and Cleveland Heart Disease were obtained from the University of California, Irvine (UCI) machine learning repository.

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“…Moreira et al proposed the modeling, performance evaluation, and comparative analysis of an artificial neural network technique called Radial Basis Function Network (RBFNN network) to identify gestational diabetes cases that may lead to multiple risks to the mother and fetus [ 6 ]. Leema et al discussed a classification framework for diagnosing diseases using a Quantum Behavioral Particle Swarm Optimization Neural Network (QPSONN) classifier, and the neural network used for classification is RBFNN [ 7 ]. Amin et al proposed an ENRBFN that approximates the unmodeled dynamics of a hovering vehicle based on an adaptive trajectory tracking controller based on the Extended Normalized Radial Basis Function (ENRBF) [ 8 ].…”