Application of Support Vector Machine Based on Particle Swarm Optimization in Classification and Prediction of Heart Disease

Abstract

This heart disease is the number one killer of Chinese residents' health. Early detection of heart disease and timely treatment are of great significance to every heart disease patient. In this article, by mining the physical index data of patients with heart disease, aiming at the problem that the optimal parameters in the traditional support vector machine model are difficult to find, particle swarm optimization is used to optimize, and a classification prediction model of heart disease based on particle swarm optimization support vector machine is established. The experimental results show that compared with the traditional support vector machine model, the optimized model improves the prediction accuracy by 1.33%, and also shortens the model training time, which helps to improve the diagnosis efficiency of heart disease.