Algorithm Refinement in the Non-Invasive Detection of Blood Glucose via Bio-RFID™ Technology1

Abstract

AbstractDiabetes Mellitus (DM) is a highly prevalent and increasingly common disorder that can have dire health consequences if not properly managed. Managing DM involves monitoring blood glucose levels, which can be cumbersome and invasive, and adherence to this practice is poor. We present a validation for a novel sensor designed to measure blood glucose (BG) non-invasively using Radio Frequency (RF) waves. In this n=5 study, we trained a Light Gradient-Boosting Machine (lightGBM) model to predict BG values using 1,555 observations from over 130 hours of data collection from five participants, where an observation is defined as data collected from 13 Bio-RFID sensor sweeps paired with a single Dexcom G6® value. Using this model, we predicted BG in the test set with a Mean Absolute Relative Difference (MARD) of 12.7% in the normoglycemic range and 14.0% in the hyperglycemic range. Overall, 70.7% of the estimates fell within 15% of the reference value, and 79.1% fell within 20% of the reference value. While this is a small participant sample, these strong initial results indicate the efficacy of this technique, and that with further refinement and more data, there is promise to achieve a clinically relevant level of accuracy.