GlucoLens: Explainable Postprandial Blood Glucose Prediction from Diet and Physical Activity
Journal:
arXiv
Published Date:
Mar 5, 2025
Abstract
Postprandial hyperglycemia, marked by the blood glucose level exceeding the
normal range after meals, is a critical indicator of progression toward type 2
diabetes in prediabetic and healthy individuals. A key metric for understanding
blood glucose dynamics after eating is the postprandial area under the curve
(PAUC). Predicting PAUC in advance based on a person's diet and activity level
and explaining what affects postprandial blood glucose could allow an
individual to adjust their lifestyle accordingly to maintain normal glucose
levels. In this paper, we propose GlucoLens, an explainable machine learning
approach to predict PAUC and hyperglycemia from diet, activity, and recent
glucose patterns. We conducted a five-week user study with 10 full-time working
individuals to develop and evaluate the computational model. Our machine
learning model takes multimodal data including fasting glucose, recent glucose,
recent activity, and macronutrient amounts, and provides an interpretable
prediction of the postprandial glucose pattern. Our extensive analyses of the
collected data revealed that the trained model achieves a normalized root mean
squared error (NRMSE) of 0.123. On average, GlucoLense with a Random Forest
backbone provides a 16% better result than the baseline models. Additionally,
GlucoLens predicts hyperglycemia with an accuracy of 74% and recommends
different options to help avoid hyperglycemia through diverse counterfactual
explanations. Code available: https://github.com/ab9mamun/GlucoLens.
