Cancer Classification using Hybrid Fast Particle Swarm Optimization with Backpropagation Neural Network

Abstract: Cancer any type of malignant growth or tumor, caused by abnormal and uncontrolled cell division: it may spread through the lymphatic system or blood stream to other parts of the body .Cancer classification is known to have the keys for addressing the problems based on cancer diagnosis and drug discovery. The proposed method of DNA microarray technique has made continuous processing thousands of gene expressions possible. Using gene expression data, the researchers have started the performance to explore the possibilities of cancer classification. The various methods have been introduced in recent years with promising results. But there are still a lot of problem which need to be addressed and understood. The performance of combining the genetic algorithm and Hybrid Fast PSO-BPN method is used to solve the optimization problems. Hybrid Fast PSO-BPN method is used to improve the accuracy and better convergence rate of genetic algorithm and this method is better for local search. This proposed method is used to overcome from the problem of computational difficulties occur by ill-condition of the square penalty function. The experimental result shows that this proposed method is better in accurate result with less execution time.

Keywords: Microarray Gene Expression Data, Feature Selection, Gene Ranking, Genetic Algorithm, and Hybrid Fast PSO-BPN.