Predictive Modeling for Hypoglycemia and Hyperglycemia Prevention with IoT

Diabetes complications like hypoglycemia and hyperglycemia must be avoided; thus, controlling blood glucose levels is essential. To prevent and monitor fluctuations in glucose levels in real-time, this paper proposes a predictive modeling approach that combines Internet of Things (IoT) technology with Support Vector Machines (SVM). Utilizing data from IoT sensors, the system uses SVM for predictive analytics on continuous glucose monitoring (CGM). Due to its reputation as a highly effective classification tool, SVM is used to predict glucose trends and promptly notify users and healthcare providers. The system improves proactive management of glucose levels by integrating SVM with IoT, which lowers healthcare costs related to diabetes care and improves patient outcomes. The proposed technique effectively accurately predicts fluctuations in blood sugar levels, as evidenced by the experimental results, which also highlight the approach's potential for personalized diabetes care in both clinical and household settings.