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Enhancing glucose measurement efficiency: a non-invasive approach using machine learning

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This approach utilizes machine learning algorithms to predict glucose levels, suggesting improved accuracy in monitoring.

The Random Forest algorithm achieved an R² value of 0.9995, showing high predictive accuracy for glucose levels.
Using two distinct datasets, the study effectively validated the model's performance across a range of glucose concentrations.
The system employs a Vector Network Analyzer to gather reflection coefficient data, optimizing the monitoring process.
These findings suggest potential applications in glucose monitoring for patients requiring non-invasive methods.

Göktaş et al. (2025) studied this question.

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