Non-Invasive Blood Glucose Monitoring via Multimodal Features Fusion with Interpretable Machine Learning

This study aimed to develop a non-invasive blood glucose estimation method by integrating wearable multimodal signals, including photoplethysmography (PPG), electrodermal activity (EDA), and skin temperature (ST), with food log–derived nutritional features, and to validate its clinical reliability. We analyzed data from 16 adults who underwent continuous glucose monitoring (CGM) while multimodal physiological signals were collected over 8–10 consecutive days, yielding more over 20,000 paired samples. Features from food logs and physiological signals were extracted, followed by feature selection using Boruta and minimum Redundancy Maximum Relevance (mRMR). Five machine learning models were trained and evaluated using five-fold cross-validation. Food log features alone demonstrated stronger predictive power than unimodal physiological signals. The fusion of nutritional, physiological, and temporal features achieved the best accuracy using LightGBM, reducing the RMSE to 12.9 mg/dL, with a MARD of 7.9%, a MAE of 8.82 mg/dL, and R2 of 0.69. SHapley Additive exPlanations (SHAP) analysis revealed that 24-h carbohydrate and sugar intake, time since last meal, and short-term EDA features were the most influential predictors. By integrating multimodal wearable and dietary information, the proposed framework significantly enhances non-invasive glucose estimation. The interpretable LightGBM model demonstrates promising clinical utility for continuous monitoring and early dysglycemia management.