Optimization of Transformer heart disease prediction model based on particle swarm optimization algorithm

TL;DR

This paper introduces an improved Transformer model optimized with particle swarm optimization for heart disease prediction, achieving higher accuracy than traditional machine learning models and demonstrating the effectiveness of PSO in this context.

Contribution

The paper presents a novel application of PSO-optimized Transformer model for heart disease prediction, surpassing traditional classifiers in accuracy.

Findings

Transformer-PSO model achieves 96.5% accuracy

PSO significantly enhances Transformer performance

Random forest achieves 92.2% accuracy

Abstract

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.