Non-Invasive Glucose Monitoring Using Optical Sensors and Machine Learning: A Predictive Model for Nutritional and Health Assessment

Glucose monitoring plays a vital role in maintaining metabolic and nutritional balance. However, invasive methods, while accurate, are often painful and impractical for routine use. This study presents a non-invasive glucose monitoring framework that integrates a high-intensity Superbright optical sensor with an IoT-enabled data acquisition system using ESP32 microcontrollers and Raspberry Pi. The IoT-based architecture enables seamless integration with cloud computing platforms, allowing remote access and scalability for large-scale population-level screening and monitoring. The system captures glucose-related optical signals, which are analyzed using various machine learning algorithms, including a novel Convolutional Neural Network–Attention Hybrid Model (CNN-AHM). CNN-AHM combines spatial feature extraction with attention-based prioritization of relevant signal patterns, enhancing both accuracy and interpretability. Among all models tested, CNN-AHM achieved the best standalone performance (RMSE = 14.06 mg/dL). When combined with complementary strength of the second-best performing Random Forest Regressor (RF-R), the system achieved a substantially improved RMSE of 7.62 mg/dL and an R² of 0.83. This work underscores the importance of integrating sensor technology, deep learning, and explainable modeling to advance non-invasive glucose monitoring solutions. Future directions include extending the architecture with explainable AI for feature level importance, and exploring real-world deployment scenarios.