Cancer prediction based on radical basis function neural network with particle swarm optimization.

Xiao-Bo Yan,Wei-Qing Xiong,Liang Hu,Kuo Zhao

doi:10.7314/apjcp.2014.15.18.7775

Xiao-Bo Yan, Wei-Qing Xiong + Show 2 more

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https://doi.org/10.7314/apjcp.2014.15.18.7775

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Abstract

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

Highlights

With the improvement of people’s living standards and industrialization in recent years, cancer incidence and mortality are significantly higher
This paper addresses cancer prediction based on radial basis function neural network optimized by particle swarm optimization
The occurrence of cancer is predicted by the means of Radial Basis Function Neural Network Optimized by Particle Swarm Optimization

Summary

Introduction

With the improvement of people’s living standards and industrialization in recent years, cancer incidence and mortality are significantly higher. Research shows that “cancer prediction” can greatly reduce cancer incidence and mortality. There are a number of research institutions and medical institutions engaged in the study of cancer prediction. Researchers around the world have done a lot of exploration and research work, an effective method of cancer prediction still have not been developed. The current methods of cancer prediction are not so satisfactory that they cannot be put into reality using, since they are too complicated or have no practical value

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Conclusion