Clu-RNN: A New RNN Based Approach to Diabetic Blood Glucose Prediction

Abstract

Diabetes is a kind of metabolic disease characterized by increased chronic blood glucose (BG) and may introduce a series of severe complications in a long run. To facilitate health management for diabetic patients, continuous monitoring and prediction of BG concentration are particularly important. Among the popular data driven solutions to BG prediction, machine learning methods, e.g. SVR, RNN and etc., utilize BG data of multiple patients to train the prediction model. However, all the training data sharing the same parameters may not be able to capture the characteristics of BG fluctuation effectively. Motivated by the fact that different subgroups of diabetic patients possess different BG fluctuation patterns, we propose a new BG prediction approach referred to as Clu-RNN based on recurrent neural networks (RNN) by incorporating a pre-process of clustering into the classical RNN. Numerical results suggest that the proposed Clu-RNN approach utilizes more than one cluster for both type I and type II diabetes and has gained improvements compared with support vector regression (SVR) and other RNN methods in terms of BG prediction accuracy.