A Method of Biomedical Information Classification Based on Particle Swarm Optimization with Inertia Weight and Mutation

With the rapid development of information technology and biomedical engineering, people can get more and more information. At the same time, they begin to study how to apply the advanced technology in biomedical information. The main research of this paper is to optimize the machine learning method by particle swarm optimization (PSO) and apply it in the classification of biomedical data. In order to improve the performance of the classification model, we compared the different inertia weight strategies and mutation strategies and their combinations with PSO, and obtained the best inertia weight strategy without mutation, the best mutation strategy without inertia weight and the best combination of the two. Then, we used the three PSO algorithms to optimize the parameters of support vector machine in the classification of biomedical data. We found that the PSO algorithm with the combination of inertia weight and mutation strategy and the inertia weight strategy that we proposed could improve the classification accuracy. This study has an important reference value for the prediction of clinical diseases.