Optimization of Transformer Heart Disease Prediction Model Based on Particle Swarm Optimization Algorithm

Abstract

Aiming at the latest particle swarm optimization algorithm, this paper proposes an improved Trans- former model to improve the accuracy of heart disease prediction and provide a new algorithm idea based on particle swarm optimization (PSO). We first use three mainstream machine learning classification algorithms - decision tree, random forest and XGBoost, and then output the confusion matrix of these three models. The results showed that the random forest model had the best performance in predicting the classification of heart disease, with an accuracy of 92.2%. Then, we apply the Transformer model based on PSO algorithm to the same dataset for the classification experiment. The results show that the classification accuracy of the model is as high as 96.5%, 4.3% higher than that of random forest, which verifies the effectiveness of PSO in optimizing Transformer model. The above research shows that PSO significantly improves Transformer performance in heart disease prediction. Improving the ability to predict heart disease is a global priority with benefits for all humankind. Accurate prediction can enhance public health, optimize medical resources, and reduce healthcare costs, leading to a healthier society. This advancement paves the way for more efficient health management and supports the foundation of a healthier, more resilient global community.