What question did this study set out to answer?

To predict blood glucose levels noninvasively using impedance spectroscopy and ensemble learning methods.

July 15, 2026Applied Sciences0 citationsOpen Access

Ensemble Learning-Based Prediction of Blood Glucose Using Impedance Spectroscopy

QGQiong GongGuilin University of Electronic Technology CXChuanpei XuGuilin University of Electronic Technology RZRuwen ZhaoBeijing Academy of Artificial Intelligence

Key Points

To predict blood glucose levels noninvasively using impedance spectroscopy and ensemble learning methods.
LASSO regression applied for feature selection
Ensemble models using Support Vector Regression, Random Forest, and LightGBM
Evaluation of regression accuracy using metrics like MAE, RMSE, and R2
Best ensemble achieved MAE of 0.9662 and RMSE of 1.3040 with R2 of 0.9414.
S1 ensemble outperformed the best individual model (MLP) by improving MAE by 9.7% and RMSE by 7.6%.
MLP outperformed S1 in the hyperglycemic range, showing RMSE of 0.7570 compared to S1's 1.1716.

Abstract

Noninvasive blood glucose monitoring using impedance spectroscopy faces challenges due to high-dimensional, redundant data and complex nonlinear relationships with glucose concentration. To address these challenges, LASSO regression was applied for feature selection, followed by ensemble learning models incorporating multiple base learners. Among them, a Stacking ensemble framework—combining Support Vector Regression (SVR), Random Forest (RF), and LightGBM—achieved the best overall regression accuracy (MAE = 0.9662, RMSE = 1.3040, R2 = 0.9414). Compared to the best individual base learner (MLP), the S1 ensemble improved overall MAE by 9.7% and RMSE by 7.6%. However, in the clinically critical hyperglycemic range, the individual MLP substantially outperformed S1 (RMSE: 0.7570 vs. 1.1716), indicating that S1’s superiority reflects a global statistical average rather than uniform improvement across all glycemic subranges. Therefore, while the proposed framework demonstrates the efficacy of combining feature selection with ensemble learning, the MLP model may be preferable for clinical applications prioritizing hyperglycemic risk identification.

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