Towards The Development of Subject-Independent Inverse Metabolic Models

Abstract

Diet monitoring is an important component of interventions in type 2 diabetes, but is time intensive and often inaccurate. To address this issue, we describe an approach to monitor diet automatically, by analyzing fluctuations in glucose after a meal is consumed. In particular, we evaluate three standardization techniques (baseline correction, feature normalization, and model personalization) that can be used to compensate for the large individual differences that exist in food metabolism. Then, we build machine learning models to predict the amounts of macronutrients in a meal from the associated glucose responses. We evaluate the approach on a dataset containing glucose responses for 15 participants who consumed 9 meals. Three techniques improve the accuracy of the models: subtracting the baseline glucose, performing z-score normalization, and scaling the amount of macronutrients by each individuals’ body mass index.