Blood Glucose Level Prediction of Diabetic Type 1 Patients Using Nonlinear Autoregressive Neural Networks

Abstract

Diabetes type 1 is a chronic disease which is increasing at an alarming rate throughout the world. Studies reveal that the complications associated with diabetes can be reduced by proper management of the disease by continuously monitoring and forecasting the blood glucose level of patients. Objective. The prior prediction of blood glucose level is necessary to overcome the lag time for insulin absorption in diabetic type 1 patients. Method. In this research, we use continuous glucose monitoring (CGM) data to predict future blood glucose level using the previous data points. We compare two neural network techniques. We apply the optimal feedforward neural network and then propose optimal nonlinear autoregressive neural networks for blood glucose prediction 15–30 minutes earlier for diabetic type 1 patients. We validate the proposed model with 2 virtual subjects using their 24-hour blood glucose level data. These two case studies have been compiled from AIDA, i.e., the freeware mathematical diabetes simulator. Results. In the prediction horizon (PH) of 15 and 30 minutes, improved results have been shown for minimal inputs for blood glucose level of a particular subject. Root mean square error (RMSE) is used for performance calculation. For the optimal feedforward neural network, the RMSE is 0.9984 and 3.78 ml/dl, and for the optimal nonlinear autoregressive neural network, it reduces the RMSE to 0.60 and 1.12 ml/dl for 15 min and 30 min prediction horizons, respectively, for subject 1. Similarly, for subject 2 for the optimal feedforward neural network, RMSE is 1.43 and 3.51 ml/dl which is improved using the optimal autoregressive neural network to 0.7911 and 1.6756 ml/dl for 15 min and 30 min prediction horizons, respectively. Validation. We further validate our proposed model using UCI machine learning datasets (Abalone and Servo), and it shows improved results on that as well. Conclusion and Future Work. The proposed optimal nonlinear autoregressive neural network model performs better than the feedforward neural network model for these time series data. In the future, we intend to investigate a greater collection of AIDA scenarios and data that are real and influence other factors of BGLs.

Evaluation of Offline Reinforcement Learning for Blood Glucose Level Control in Type 1 Diabetes

“…For safety reasons, biomedical experiments with machine learning algorithms have been done and pre-evaluated in silico through computer simulation. Currently, there are several T1D simulators available, with both free and paid versions, as for instance, AIDA [22], Type 1 Diabetes Virtual Patient Population (T1D-VPP) [23], and the UVA/PADOVA Simulator [24]. AIDA is a free software simulating human plasma insulin and blood glucose for education and research purposes.…”

A deep neural network prediction method for diabetes based on Kendall’s correlation coefficient and attention mechanism

“…It’s notable that some scholars have ventured into using deep learning methods for diabetes prediction research [ 8 ]. Deep learning proves advantageous in handling complex nonlinear data, as it has the capability to automatically learn feature representations, consequently improving prediction accuracy [ 9 ]. Deep learning models can be highly sensitive to the representation of input data and often require careful consideration of feature selection to optimize performance.…”

Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems

“…In fact, obtaining mathematical models that can adequately capture human physiological behavior for use in control schemes has been a major challenge [ 13 ]. Vettoretti et al [ 14 ] stress that, due to the huge amount of data collected from T1DM patients using CGM, AI techniques can give support to bolus insulin calculation [ 15 ] and BGC prediction [ 16 , 17 , 18 , 19 ], that is, predict future glycemic profiles based on present and past values of glycemia. Furthermore, reinforcement learning (RL) has also been applied in the context of AID systems with an increasing interest in the past few years [ 20 , 21 , 22 ].…”