Cancer Prediction Based on Radical Basis Function Neural Network with Particle Swarm Optimization


This paper addresses cancer prediction based on radial basis function neural network optimized by particleswarm optimization. Today, cancer hazard to people is increasing, and it is often difficult to cure cancer. Theoccurrence of cancer can be predicted by the method of the computer so that people can take timely and effectivemeasures to prevent the occurrence of cancer. In this paper, the occurrence of cancer is predicted by the meansof Radial Basis Function Neural Network Optimized by Particle Swarm Optimization. The neural networkparameters to be optimized include the weight vector between network hidden layer and output layer, andthe threshold of output layer neurons. The experimental data were obtained from the Wisconsin breast cancerdatabase. A total of 12 experiments were done by setting 12 different sets of experimental result reliability. Thefindings show that the method can improve the accuracy, reliability and stability of cancer prediction greatlyand effectively.