CONTINUOUS BLOOD GLUCOSE MONITORING, BLOOD GLUCOSE LEVEL PREDICTION AND INSULIN SUGGESTION FOR DIABETIC TYPE 1 PATIENTS OR ARTIFICIAL PANCREAS (AP)

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. In the recent past, numerous researches have been carried out to monitor blood glucose level which suggests the quantity of insulin i.e. artificial pancreas. 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 propose optimal nonlinear autoregressive neural networks. We compared optimal feedforward neural network with 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 diabetic patients using their 24 hours blood glucose level data. 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 optimal feedforward neural network, the RMSE is 0.9984 and 3.78ml/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 horizon respectively for subject 1. Similarly, for subject 2 the optimal feedforward neural network, RMSE is 1.43 and 3.51ml/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 horizon, respectively. Validation: We further validate our proposed model using UCI machine learning datasets 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 patients, and other factors of BGLs.