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.
Highlights
type 1 diabetes (T1D) is basically a chronic disease which is caused by insulin’s deficiency. e reason for this deficiency is the destruction of pancreatic β-cells which can further result in affecting the capability of the pancreas to produce sufficient insulin
We intend to investigate a greater collection of automatic insulin delivery advisor (AIDA) scenarios and data that are real and influence other factors of BGLs
We discovered that nonlinear autoregressive (NAR) neural networks are powerful computational models for modeling and forecasting nonlinear time series data [3, 4].We proposed optimal nonlinear autoregressive neural networks for blood glucose prediction in diabetic type 1 patients
Summary
T1D is basically a chronic disease which is caused by insulin’s deficiency. e reason for this deficiency is the destruction of pancreatic β-cells which can further result in affecting the capability of the pancreas to produce sufficient insulin. T1D can be very harmful to the health of a person, and nowadays, it is progressively increasing at the rate of 3% per year [1, 2] It usually takes 60–120 minutes to absorb the insulin to normalize the blood glucose level. We analyzed different feedforward neural techniques from the literature used for blood glucose level prediction using univariate and multivariate time series data of diabetic patients and neural networks used for other applications. We discovered that nonlinear autoregressive (NAR) neural networks are powerful computational models for modeling and forecasting nonlinear time series data [3, 4].We proposed optimal nonlinear autoregressive neural networks for blood glucose prediction in diabetic type 1 patients. E organization of the paper is as follows: Section 2 contains related work, Section 3 describes the system architecture, Section 4 presents the model description and BGL performance prediction, and Section 5 shows results and analysis, whereas in Section 6, there is conclusion presenting the summary of the work done in this research and future work suggested
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