ISBN: 978-981-18-3959-7 DOI: 10.18178/wcse.2022.06.021
A Multi-Network Fusion Prediction Method for Patient Blood Glucose Concentration Prediction and Hyper/Hypoglycemia Blood Glucose Warning
Abstract—To cope with the inconvenience as well as potential dangers of glycemic management in diabetic patients, we propose a fully automatic blood glucose prediction method based on neural networks. This method adopts a voting strategy to merge the advantages of regression prediction and classification prediction to construct a multi-network fusion prediction model, which can achieve the following dual objectives: (1) hyperglycemia and hypoglycemia warning with a time interval of 30 minutes, while the warning results have high reliability; (2) real-time blood glucose concentration prediction. To verify the effectiveness of our method, we tested 19 simulated patients in UVA/Padova T1DMS. The results show that our method can achieve the objectives well, and it also has better performance than the commonly used single neural network method.
Index Terms—diabetes, neural network, blood glucose, prediction algorithms
Yanran Wang, Wenping Liu, Haoyu Jin
Institute of Medical Devices, Guangdong Food and Drug Vocational College, Guangzhou, Guangdong, CHINA
Cite: Yanran Wang, Wenping Liu, Haoyu Jin, "A Multi-Network Fusion Prediction Method for Patient Blood Glucose Concentration Prediction and Hyper/Hypoglycemia Blood Glucose Warning, " Proceedings of 2022 the 12th International Workshop on Computer Science and Engineering (WCSE 2022), pp. 146-151, June 24-27, 2022.