XGBoost Calibration Considering Feature Importance for Noninvasive HbA1c Estimation Using PPG Signals

Abstract

Diabetes has recently become a more serious disease. Almost every family has at least one diabetic. Patients have to regularly monitor their blood glucose levels, and using an invasive device on the other hand can be really painful and less reliable. This is because blood glucose levels fluctuate more with food intake. On the contrary, HbA1c level does not fluctuate as much as that of blood glucose. Therefore, in this study, XGBoost calibration considering only important features for Monte-Carlo simulation based noninvasive HbA1c estimation with PPG signals was proposed. After considering the important 13 of the 45 features, the model achieved a Pearson's r value of 98.90%.