1School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China 2Mobile Health Management System Engineering Research Center of the Ministry of Education, Hangzhou 311121, China 3The Affiliated Hospital of Hangzhou Normal University, Hangzhou 310015, China
[Objective] This paper tries to accurately predict the risk of coronary heart disease and analyze the importance of different factors of coronary heart disease, which helps doctors timely intervene and effectively support patients in prevention and treatment. [Methods] We proposed a coronary heart disease prediction framework based on an attention-interpretable tabular learning neural network (PCHD-TabNet). We used self-supervised learning to help the model accelerate convergence and maintain stability. [Results] The overall performance of PCHD-TabNet was better than other models, and the AUC of the dataset reached 0.72. [Limitations] Framingham data set is routine physical examination data. If there are better clinical data, the predictive performance may be further improved. [Conclusions] Comparative experiments show that the proposed method improves the model’s performance and is superior to other traditional models. This study provides an efficient method for coronary heart disease prediction. It also serves as a reference for similar data mining tasks.
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