Glucose Prediction with Long Short-Term Memory (LSTM) Models in Three Distinct Populations
Cleber F. Carvalho, Zilu Liang
Abstract
Diabetes mellitus is a chronic metabolic disorder characterized by the dysregulation of blood glucose, which can lead to a range of serious health complications if not properly managed. Continuous glucose monitoring (CGM) is a cutting-edge technology that tracks glucose levels in real time, providing continuous and detailed information about glucose fluctuations throughout the day. The CGM data can be leveraged to train deep learning models forecasting blood glucose levels. Several deep learning-based glucose prediction models have been developed for diabetes populations, but their generalizability to other populations such as prediabetic individuals remains largely unknown. Prediabetes is a condition where blood glucose levels are higher than normal but not yet high enough to be classified as diabetes. It is a critical stage where intervention can prevent the progression to type 2 diabetes. To fill in the knowledge gap, we developed Long Short-Term Memory (LSTM) glucose prediction models tailored to three distinct populations: type 1 diabetes (T1D), type 2 diabetes (T2D), and prediabetic (PRED) individuals. We evaluated the internal and external validity of these models. The results showed that the model constructed with the prediabetic dataset demonstrated the best internal and external validity in predicting glucose levels across all three test sets, achieving a normalized RMSE (NRMSE) of 0.21 mg/dL, 0.11 mg/dL, and 0.25 mg/dL when tested on the prediabetic, T1D, and T2D test sets, respectively.