Non-Invasive Glucose Prediction System Enhanced by Mixed Linear Models and Meta-Forests for Domain Generalization

TL;DR

This paper introduces a non-invasive glucose prediction system combining NIR and mm-wave sensing, utilizing mixed linear models and meta-forests to improve accuracy and domain generalization for personalized diabetes monitoring.

Contribution

It presents a novel integration of Mixed Linear Models and Meta-Forests for enhanced domain generalization in non-invasive glucose prediction.

Findings

Achieved MAE of 17.47 mg/dL in glucose prediction

Demonstrated improved accuracy on unseen subjects

Validated the effectiveness of domain generalization techniques

Abstract

In this study, we present a non-invasive glucose prediction system that integrates Near-Infrared (NIR) spectroscopy and millimeter-wave (mm-wave) sensing. We employ a Mixed Linear Model (MixedLM) to analyze the association between mm-wave frequency S_21 parameters and blood glucose levels within a heterogeneous dataset. The MixedLM method considers inter-subject variability and integrates multiple predictors, offering a more comprehensive analysis than traditional correlation analysis. Additionally, we incorporate a Domain Generalization (DG) model, Meta-forests, to effectively handle domain variance in the dataset, enhancing the model's adaptability to individual differences. Our results demonstrate promising accuracy in glucose prediction for unseen subjects, with a mean absolute error (MAE) of 17.47 mg/dL, a root mean square error (RMSE) of 31.83 mg/dL, and a mean absolute percentage error (MAPE) of 10.88%, highlighting its potential for clinical application. This study marks a significant step towards developing accurate, personalized, and non-invasive glucose monitoring systems, contributing to improved diabetes management.