A combined-predictor approach to glycaemia prediction for type 1 diabetes

Abstract

Glycaemia prediction plays a vital role in preventing complications related to diabetes mellitus type 1, supporting physicians in their clinical decisions and motivating diabetics to improve their everyday life. Several algorithms, such as mathematical models or neural networks, have been proposed for blood glucose prediction. An approach of combining several glycaemia prediction models is proposed. The main idea of this framework is that the outcome of each prediction model becomes a new feature for a simple regressive model. This approach can be applied to combine any blood glycaemia prediction algorithms. As an example, the proposed method was used to combine an Autoregressive model with exogenous inputs, a Support Vector Regression model and an Extreme Learning Machine for regression model. The multiple-predictor was compared to these three prediction algorithms on the continuous glucose monitoring system and insulin pump readings of one type 1 diabetic patient for one month. The algorithms were evaluated in terms of root-mean-square error and Clarke error-grid analysis for 30, 45 and 60, min prediction horizons.