Dynamic Predictive Analysis of Blood Glucose Levels using CNN with Real-time Feedback Mechanism

Within the domain of diabetes management, predicting blood glucose levels accurately and dynamically plays a major role in providing effective treatment and prevention of complications. This research work introduces a novel a strategy that makes use of convolutional neural networks (CNNs) to predict diabetic blood glucose values patients. Unlike static models, our method incorporates a real-time feedback mechanism, allowing for dynamic adjustments based on evolving physiological and environmental factors. Leveraging extensive datasets encompassing patient demographics, lifestyle factors, and physiological indicators, our CNN-based model demonstrates superior accuracy and adaptability. Furthermore, incorporating a real-time feedback mechanism empowers clinicians and patients with timely insights, enabling proactive interventions help keep blood glucose levels at their ideal levels and mitigate risks of hypo- or hyperglycemia events. Through performance evaluation and validation against standard clinical practices, our approach showcases promising advancements in personalized diabetes care, heralding a new era of precision medicine in chronic disease management.