Abstract：Cardiovascular disease has become a major public health problem worldwide,and its prediction and early intervention are of great significance in reducing its morbidity and mortality. This study aimed to propose a Transformer-based cardiovascular disease prediction model to overcome the limitations of traditional models in modeling complex dependencies among multiple clinical features. The model used 13 physiological parameters (such as age, blood sugar, cholesterol, chest pain type, etc.) for standardization and encoding processing, and applied the self-attention mechanism of the Transformer to model the global dependencies among multidimensional clinical features, thereby improving the extraction of complex feature interactions.Theexperimentaldata were divided into a training set and a test set, and the modelwas trained and tested on the data. Experimental results showedthat the accuracy of the proposed Transformer modelwas 98.68% on the training set and 94.68% on the test set, whichwas 8% higher than the accuracy of traditional convolutional neural networks and deep neural networks. The loss rate of the Transformer model on the training set gradually tended to 0, which is significantly better than the loss rate of 0.13 of the CNN model and the loss rate of the DNN model of 0.22; the loss rate of the Transformer model on the test set tended to 0.1, whichwas also significantly better than that of the CNN model of 0.3 and that of the DNN model of 0.4.In conclusion, the cardiovascular disease prediction model based on Transformer made rapid diagnosis through easy-to-obtain clinical data with high accuracy,providing strong technical support for early intervention and precise treatment and displaying application prospects and practicability.
张培龙, 赵丰, 毕日格格, 司一丹, 张浩力, 图瓦, 李华. 基于Transformer的心血管疾病预测模型研究[J]. 中国生物医学工程学报, 2026, 45(3): 321-331.
Zhang Peilong, Zhao Feng, Bi Rigege, Si Yidan, Zhang Haoli, Tu Wa, Li Hua. Research on Cardiovascular Disease Prediction Model Based on Transformer. Chinese Journal of Biomedical Engineering, 2026, 45(3): 321-331.
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