Deep Learning Techniques for Predicting Hypoglycemic Events in Diabetic Patients

This study investigates the application of deep learning, particularly Recurrent Neural Networks (RNNs), to predict hypoglycemic events in diabetic patients. Using continuous glucose monitoring data, the research aims to improve patient comfort and reduce the need for emergency interventions. Deep learning models are trained on high-quality, real-world data, focusing on short-term prediction horizons to enable timely preventive actions. The results demonstrate the potential of these models to accurately predict hypoglycemia, significantly improving diabetes management and patient quality of life. This research contributes to the field of Ambient Assisted Living (AAL) by integrating advanced AI techniques into chronic disease management, highlighting the significant role of AI and machine learning in healthcare innovation.