944-P: Blood Glucose Prediction for Type 2 Diabetic Patients Using Machine Learning

Abstract

The blood glucose prediction algorithms were widely studied for the patients with type 1 diabetes. We analyzed the data of 16 type 2 diabetic patients underwent continuous glucose monitoring (CGM) through deep-learning models. Feed Forward Neural Network (FFNN) and Long Short-Term Memory (LSTM) were used to model and predict the blood glucose. Blood glucose levels after 5, 15, 30, and 45 min were predicted for each patient. To evaluate the predictive power of model, the Root Mean Square Error (RMSE) was calculated by comparing the actual blood glucose and predictive blood glucose. The value of RMSE by FFNN was lower than that by LSTM. The predictive success rates for the glycemic excursion at 5 minutes by FFNN, LSTM were 96% and 95%, the predictive power of two models gradually decreased with time (Figure 1). These results suggest that the well trained deep-learning model would be possible to predict the harmful glycemic events among type 2 diabetic patient. Disclosure D. Kim: None. H. Lee: None. Y. Kim: None. S. Kim: None. S. Lee: None. S. Chun: None.