Noninvasive Blood Glucose Measurement Using RF Spectroscopy and a LightGBM AI Model

We present a validation for a novel sensor and data processing pipeline designed to measure blood glucose (BG) noninvasively using the rapid collection of a broad range of radio frequency (RF) waves via a decoupled antenna array. Five healthy human subjects ingested 37.5 g of glucose solution to generate BG readings across two glycemic ranges: normoglycemic and hyperglycemic. Concurrent measurements from a continuous glucose monitor (CGM) and the RF sensor were collected for comparative analysis. A light gradient-boosting machine (LightGBM) model was trained to predict BG values using 1555 observations, where an observation is defined as data collected from 13 RF sensor sweeps paired with a single Dexcom G6 CGM value. Using this model, we predicted BG in the held-out test dataset with a mean absolute relative difference (MARD) of 12.7% in the normoglycemic range and 14.0% in the hyperglycemic range. While in early-stage validation, these results demonstrate the promise of this hardware and software technique for the noninvasive measurement of BG for practical application.