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

2024-12-03

AI Summary

To address the clinical need for early and precise prediction of cardiovascular and cerebrovascular diseases, this study proposes a novel joint optimization framework that integrates Particle Swarm Optimization (PSO) with the Transformer architecture—marking the first application of PSO to concurrently optimize both hyperparameters and architectural configurations of Transformers. Evaluated on a unified clinical dataset, the PSO-Transformer achieves a classification accuracy of 96.5%, outperforming the best-performing baseline (Random Forest, 92.2%) by 4.3 percentage points. Comprehensive evaluation via confusion matrix analysis and other standard metrics confirms that the proposed method substantially surpasses existing models in predictive accuracy. This work introduces a structural innovation in algorithm design specifically tailored for medical risk prediction, delivering a robust, interpretable, and clinically actionable solution for cardiac risk assessment and optimized allocation of healthcare resources.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Machine Learning: Optimization

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Heart Disease Prediction
• Model Accuracy
• Health Resource Optimization

Innovation

• Particle Swarm Optimization
• Transformer Model
• Cardiovascular Disease Prediction