| Abstract: | Diabetes, as a chronic disease, is caused by the increase of blood glucose concentration due to pancreatic insulin production failure or insulin resistance in the body. Predicting the change trend of blood glucose level in advance brings convenience for prompt treatment, so as to maintain blood glucose level within the recommended levels. Based on the flash glucose monitoring data, we propose a method that combines prophet with temporal convolutional networks (TCN) to achieve good experimental results in predicting patient blood glucose. The proposed model achieves high accuracy in the long-term and short-term prediction of blood glucose, and outperforms other models on the adaptability to non-stationary and detection capability of periodic changes. |
