Time Series Glucose Level Detection in fuel-cell based sensors Using Machine Learning: A Comparative Study of K-NN...

Fuel-cell-based printed sensors offer a self-powered solution for glucose monitoring, ideal for portable and wearable applications. However, variability in current signals from glucose oxidation complicates accurate detection. To address this issue, this study investigates the application of machine learning algorithms to enhance glucose prediction accuracy. Specifically, we compare k-Nearest Neighbors paired with Dynamic Time Warping and eXtreme Gradient Boosting, in order to determine their effectiveness in handling signal variability and improving prediction robustness. The results demonstrate the strengths of both approaches for glucose monitoring, although k-Nearest Neighbors achieved a superior performance, yielding an adjusted R² of 0.86 and an MSE of 1.76. This improvement may be attributed to the Dynamic Time Warping's ability to effectively capture temporal variations in the glucose oxidation signal.Clinical Relevance- This study demonstrates the potential of machine learning-enhanced fuel-cell-based printed sensors for accurate glucose monitoring, offering a reliable, portable, and cost-effective solution that could improve diabetes management in clinical and wearable healthcare settings.