Neural network classifier optimization using Differential Evolution with Global Information and Back Propagation algorithm for clinical datasets

Abstract

A Computer-Aided Diagnostic (CAD) system that uses Artificial Neural Network (ANN) trained by drawing in the relative advantages of Differential Evolution (DE), Particle Swarm Optimization (PSO) and gradient descent based backpropagation (BP) for classifying clinical datasets is proposed. The DE algorithm with a modified best mutation operation is used to enhance the search exploration of PSO. The ANN is trained using PSO and the global best value obtained is used as a seed by the BP. Local search is performed using BP, in which the weights of the Neural Network (NN) are adjusted to obtain an optimal set of NN weights. Three benchmark clinical datasets namely, Pima Indian Diabetes, Wisconsin Breast Cancer and Cleveland Heart Disease, obtained from the University of California Irvine (UCI) machine learning repository have been used. The performance of the trained neural network classifier proposed in this work is compared with the existing gradient descent backpropagation, differential evolution with backpropagation and particle swarm optimization with gradient descent backpropagation algorithms. The experimental results show that DEGI-BP provides 85.71% accuracy for diabetes, 98.52% for breast cancer and 86.66% for heart disease datasets. This CAD system can be used by junior clinicians as an aid for medical decision support.