Automatic Detection of Excessive Glycemic Variability for Diabetes Management

Abstract

Glycemic variability, or fluctuation in blood glucose levels, is a significant factor in diabetes management. Excessive glycemic variability contributes to oxidative stress, which has been linked to the development of long-term diabetic complications. An automated screen for excessive glycemic variability, based on the readings from continuous glucose monitoring (CGM) systems, would enable early identification of at risk patients. In this paper, we present an automatic approach for learning variability models that can routinely detect excessive glycemic variability when applied to CGM data. Naive Bayes (NB), Multilayer Perceptron (MP), and Support Vector Machine (SVM) models are trained and evaluated on a dataset of CGM plots that have been manually annotated with respect to glycemic variability by two diabetes experts. In order to alleviate the impact of noise, the CGM plots are smoothed using cubic splines. Automatic feature selection is then performed on a rich set of pattern recognition features. Empirical evaluation shows that the top performing model obtains a state of the art accuracy of 93.8%, substantially outperforming a previous NB model.