融合CNN与Transformer，从生活史数据中精准识别心脏病风险因素。
A Hybrid CNN-Transformer Model for Heart Disease Prediction Using Life History Data
- 用CNN捕捉局部特征，Transformer建模全局关系，协同处理高维数据。
- 在准确率、精确率和召回率上均优于SVM、CNN和LSTM等传统模型。
- 适合个性化医疗与健康管理场景，为心脏病早期预测提供新方法。
本研究提出一种融合卷积神经网络（CNN）与Transformer的混合模型，用于基于高维生活史数据预测和诊断心脏病。利用CNN检测局部特征、Transformer感知全局关联的优势，该模型能有效识别心脏病的风险因素。实验结果表明，该模型在准确率、精确率和召回率等多项指标上均优于支持向量机（SVM）、CNN和长短期记忆网络（LSTM）等基准模型，展现出对多维非结构化数据的强大处理能力。通过移除部分模块的对比实验验证了双模块协同的重要性。论文还探讨了未来引入额外特征与方法以提升模型在不同条件下的适应性。研究为机器学习在心脏病预测中的应用提供了新思路，具有广泛应用于个性化医疗与健康管理的潜力。
原文摘要 · Abstract (English)
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.
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