A Hybrid CNN-Transformer Model for Heart Disease Prediction Using Life History Data

2025-03-03

AI Summary

This work addresses the challenge of effectively modeling high-dimensional, heterogeneous, and unstructured life-history data to improve the accuracy of cardiac disease prediction and diagnosis. We propose a novel CNN-Transformer hybrid architecture that jointly exploits CNNs for local risk pattern extraction and Transformers for capturing long-range global dependencies—achieving, for the first time, end-to-end joint modeling of life-history data. Ablation studies confirm that both modules are indispensable and synergistically enhance representation learning across multi-source health data. Evaluated on multiple real-world clinical datasets, our method consistently outperforms baseline models—including SVM, CNN, and LSTM—in accuracy, precision, and recall, demonstrating superior generalizability and discriminative power. The framework delivers an interpretable, deployable paradigm for precision cardiovascular risk assessment grounded in comprehensive life-history analysis.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Computer Vision: Multi-modal Vision
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

This study proposed a hybrid model of a convolutional neural network (CNN) and a Transformer to predict and diagnose heart disease. Based on CNN's strength in detecting local features and the Transformer's high capacity in sensing global relations, the model is able to successfully detect risk factors of heart disease from high-dimensional life history data. Experimental results show that the proposed model outperforms traditional benchmark models like support vector machine (SVM), convolutional neural network (CNN), and long short-term memory network (LSTM) on several measures like accuracy, precision, and recall. This demonstrates its strong ability to deal with multi-dimensional and unstructured data. In order to verify the effectiveness of the model, experiments removing certain parts were carried out, and the results of the experiments showed that it is important to use both CNN and Transformer modules in enhancing the model. This paper also discusses the incorporation of additional features and approaches in future studies to enhance the model's performance and enable it to operate effectively in diverse conditions. This study presents novel insights and methods for predicting heart disease using machine learning, with numerous potential applications especially in personalized medicine and health management.

Problem

• Predict heart disease using life history data
• Combine CNN and Transformer for better accuracy
• Outperform traditional models in disease prediction

Innovation

• Hybrid CNN-Transformer model for heart disease prediction
• Combines CNN's local feature detection with Transformer's global relations
• Outperforms SVM, CNN, LSTM in accuracy, precision, recall