Glycaemic index prediction : a pilot study of data linkage challenges and the application of machine learning

Abstract

The glycaemic index (GI) is widely used to characterize the effect that a food has on blood glucose which is of major importance to diabetic individuals as well as the general population at large. At present, its applicability is severely limited by the labour involved in its measurement and the lack of understanding about how different foods interact to produce the GI of the meal comprising them. In this pilot study we examine if readily available biochemical properties of food scan be used to predict their GI, thus opening possibilities for practicable use of the GI in the management of blood glucose in everyday life. We also examine practical challenges in the cross-linking of food information sources collected by different organizations, and highlight the need for the development of a universal standard which would facilitate automatic and error free data integration.