Abstract: In view of the complexity and instability of blood glucose data in diabetic patients, this paper presents a short-term blood glucose prediction method based on K-means clustering algorithm using RBF neural network. Firstly, the blood glucose concentration time series collected by CGMS is filtered and normalized to improve the smoothness of the blood glucose data sequence and weaken the randomness of the original blood glucose data sequence. Then the RBF network is constructed on the processed blood glucose concentration time series. The K-means clustering is used to optimize, and the weights of the RBF network are adjusted by the least square method to obtain the predicted value of the future blood glucose concentration, thereby ensuring the accuracy of the prediction.

Key words:  blood glucose prediction, time series, RBF neural network, K-means clustering algorithm