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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2503.02124 |
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| _version_ | 1866909524087013376 |
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| author | Hao, Ran Xiang, Yanlin Du, Junliang He, Qingyuan Hu, Jiacheng Xu, Ting |
| author_facet | Hao, Ran Xiang, Yanlin Du, Junliang He, Qingyuan Hu, Jiacheng Xu, Ting |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02124 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Hybrid CNN-Transformer Model for Heart Disease Prediction Using Life History Data Hao, Ran Xiang, Yanlin Du, Junliang He, Qingyuan Hu, Jiacheng Xu, Ting Machine Learning 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. |
| title | A Hybrid CNN-Transformer Model for Heart Disease Prediction Using Life History Data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.02124 |