Comprehensive benchmarking of machine learning models for blood glucose classification and prediction: new approach for improved hyperglycemia and hypoglycemia detection and prediction

Background: Diabetes mellitus affects 463 million people worldwide and necessitates continuous blood glucose monitoring. Current glucose prediction systems often lack efficiency, and real-time prediction is essential for timely clinical intervention. Objectives: This study aims to develop and validate a novel Convolutional Recurrent Neural Network (CRNN) enhanced with bio-inspired algorithms to improve blood glucose prediction and enable real-time detection of hypoglycemia and hyperglycemia. Materials and methods: The proposed framework employs a CRNN architecture that combines Convolutional Neural Networks (CNNs) for feature extraction with Long Short-Term Memory (LSTM) layers for temporal sequence learning. The model was trained and evaluated using the HUPA-UCM diabetes dataset. Additionally, the study benchmarks the proposed model against 19 traditional Machine Learning (ML) algorithms and compares it with state-of-the-art methods from the literature. Results: The proposed approach demonstrates superior predictive capability, consistently delivering promising results across multiple evaluation frameworks. The model achieves clinically acceptable prediction intervals, confirming its effectiveness in enhancing the accuracy and reliability of blood glucose prediction for diabetes management. Conclusion: The findings demonstrate that the proposed CRNN model, enhanced with bio-inspired algorithms, provides an effective and reliable solution for real-time blood glucose prediction. By outperforming conventional ML methods and achieving clinically acceptable accuracy levels, the model shows strong potential for integration into intelligent diabetes management systems to support timely clinical decisions and improve patient outcomes.