Physiological Signal Based Blood Glucose Prediction in Diabetics and Non-Diabetics

Effective blood glucose regulation is essential for both diabetic and non-diabetic individuals. Finger-prick methods are inconvenient and painful, while continuous glucose monitoring (CGM) systems are costly and require frequent sensor replacements. This paper proposes an innovative approach for continuous real-time blood glucose level estimation using non-invasive physiological signals and ensemble regression techniques. We observed significant differences in the predictive power of individual features and overall model performance between the diabetic and the non-diabetic groups. The best model for the diabetic group achieved better performance (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.50) compared to the best model for the non-diabetic group (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.27). However, when evaluated for clinical accuracy, the non-diabetic models outperformed the diabetic models. The best non -diabetic model had 100% of predictions in the clinically acceptable zones of the Clarke Error Grids (CEG), while the best diabetic model had only 91%. The between-group differences in model performance are likely due to the variations in underlying glucose dynamics between diabetic and non-diabetic individuals. Our findings highlight the necessity of collecting and sharing large multimodal datasets from both diabetic and non-diabetic populations, as well as evaluating glucose prediction models from both mathematical and clinical accuracy.