Non-Invasive Glucose Measurement Using Radio-Frequency Spectroscopy and Machine Learning
Bibliographic record
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 and tested on data collected on separate days, ensuring that the training and test sets are drawn from distinct, non-overlapping time periods. 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 perception 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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".