Deep learning based one step and multi‐steps ahead forecasting blood glucose level

Abstract

AbstractEnabling diabetic patients to predict their Blood Glucose Levels (BGL) is a crucial aspect of managing their metabolic condition, as it allows them to take appropriate measures to avoid hypo or hyperglycemia. Machine Learning (ML) and Deep Learning (DL) techniques have made this possible, and this paper evaluates and compares the performance of five distinct ML/DL models including: Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Support Vector Regression (SVR), Gated Reccurent Unit (GRU) and Deep Belief Network (DBN) for forecasting BGL, by applying two different forecasting methods, namely One Step Ahead (OSF) and Multi‐Step Ahead (MSF) comprising five different variants. The performance is evaluated based on four metrics: Mean Absolute Error (MAE), Mean Magnitude Relative Error (MMRE), Root Mean Square Error (RMSE) and Predictive Level (PRED). Additionally, the statistical significance of the regressors was evaluated using the Scott‐Knott (SK) test, while the Borda Count (BC) voting system was employed to rank them. The results indicate that the best performance was achieved with OSF using GRU. Furthermore, the effectiveness of an MSF strategy depends on the ML/DL technique used, and the best combinations were DBN with DirRec, DBN with Recursive, SVR with Recursive and SVR with DirRec. Additionally, DirRec was found to be the best strategy, as it consistently ranked first regardless of the ML/DL technique used.