Non-invasive Glucose Measurement using Radio-Frequency Spectroscopy and Machine Learning

Alishba Fatmi, Subhash Nerella, Dominic Klyve, Parisa Rashidi, Adam Khalifa

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Keywords: Continuous Glucose Monitoring, machine learning, non-invasive

Abstract: Continuous glucose monitoring (CGM) devices provide critical real-time data but remain minimally invasive and require frequent replacement. This study presents a novel, personalized machine learning approach for non-invasive glucose monitoring using radiofrequency (RF) spectroscopy to address these limitations. To simulate real-world usage and ensure clinical relevance, we developed a model for a single individual using data collected during standardized meals. The model was trained using a full day of data and validated on a completely separate day to prevent data leakage. A comprehensive machine learning pipeline was validated using 3,101 spectral features (400–3500 MHz) combined with contextual data to predict glucose levels. Our best-performing model, multi-layer perceptron regressor (MLP), achieved a Mean Absolute Relative Difference (MARD) of 11.6%. These findings demonstrate that a personalized machine learning model holds potential to predict glucose non-invasively. This highlights a promising path toward a more user-friendly and sustainable solution for continuous glucose management

Track: 3. Signal processing, machine learning, deep learning, and decision-support algorithms for digital and computational health

NominateReviewer: Subhah Nerella [e-mail]

Submission Number: 136