A Continuous Multiple-Timestep Blood Glucose Level Prediction System Using Stacked LSTM for High-Accuracy Hypoglycemia Alerts in Smart Contact Lenses

A continuous multiple-timestep blood glucose level prediction system based on stacked long short-term memory (LSTM) is proposed for high-accuracy hypoglycemia alerts in smart contact lenses. A deeper representation of input data is provided by using stacked LSTM rather than conventional vanilla LSTM, enabling higher accuracy. Python programming with the Keras library was implemented to evaluate the proposed system. The results showed a 16% improvement compared to the conventional approach.