Aiming at the latest particle swarm optimization algorithm, this paper proposes an improved Transformer model to improve the accuracy of heart disease prediction and provide a new algorithm idea. 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 particle swarm optimization (PSO) algorithm to the same dataset for classification experiment. The results show that the classification accuracy of the model is as high as 96.5%, 4.3 percentage points higher than that of random forest, which verifies the effectiveness of PSO in optimizing Transformer model. From the above research, we can see that particle swarm optimization 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 healthier populations and more productive societies worldwide. This advancement paves the way for more efficient health management and supports the foundation of a healthier, more resilient global community.

翻译：针对最新的粒子群优化算法，本文提出一种改进的Transformer模型，旨在提高心脏病预测的准确性，并提供新的算法思路。我们首先采用三种主流的机器学习分类算法——决策树、随机森林和XGBoost，并输出这三种模型的混淆矩阵。结果表明，随机森林模型在心脏病分类预测中表现最佳，准确率达到92.2%。随后，我们将基于粒子群优化（PSO）算法的Transformer模型应用于相同数据集进行分类实验。结果显示，该模型的分类准确率高达96.5%，较随机森林提升4.3个百分点，验证了PSO在优化Transformer模型方面的有效性。从上述研究可以看出，粒子群优化显著提升了Transformer在心脏病预测中的性能。提升心脏病预测能力是全球优先事项，对全人类具有重要价值。精准预测能够促进公共卫生、优化医疗资源配置并降低医疗成本，从而推动全球人口健康水平提升与社会生产力发展。这一进展为更高效的健康管理开辟了道路，并为构建更健康、更具韧性的全球社会奠定基础。