基于K-均值聚类的RBF神经网络血糖浓度预测

针对糖尿病患者血糖数据的复杂性与不稳定性，提出一种基于K-均值聚类算法的径向基函数（Radical Basis Function， RBF）神经网络的短期血糖预测方法。首先将动态血糖监测(Continous Glucose Monitoring System， CGMS)采集的糖尿病患者血糖浓度时间序列进行平滑滤波和归一化处理，提高血糖数据序列的光滑度，弱化原始血糖数据序列的随机性。然后对处理后的血糖浓度时间序列构造RBF网络，采用K-均值聚类进行优化，并用最小二乘法进行RBF网络的权值调整进而获得未来血糖浓度的预测值，从而保证预测的精度。

Prediction of Blood Glucose Based on K-means Clustering Algorithm with RBF Neural Network

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.