AI-Based Edge-Intelligent Hypoglycemia Prediction System Using Alternate Learning and Inference Method for Blood Glucose Level Data with Low-periodicity

In this study, we developed an AI-based edge-intelligent hypoglycemia prediction system for the environment with low-periodic blood glucose level. By using long-short-term memory (LSTM), a specialized network for handling time series data among neural networks along with introducing alternate learning and inference, it was possible to predict the BG level with high accuracy. In order to achieve, the system for predicting the blood glucose level was created using LSTM, and the performance of the system was evaluated using the method of the classification problem. The system was successfully predicted the probability of occurrence of hypoglycemia after 30 min at approximately 80% times. Furthermore, it was demonstrated that accuracy is improved by alternately performing learning and prediction.