Continuous blood glucose monitoring prediction for diabetes using evolving neural network

This research presents a new evolving neural network approach to forecast blood glucose for people with diabetes. The accuracy of forecasting using the proposed evolving neural network is demonstrated to outperform a conventional back propagation neural network. People with diabetes need to control their blood sugar levels. High blood sugar over long term leads to many other health complications. To avoid high blood sugar, it is important for people to be able to predict what will happen to blood sugar so that they can do something to prevent hypo or hyper glycaemia. However, many external uncontrollable factors can make blood glucose difficult to predict, such as meals which increase carbs and glucose goes up. Exercise also affects blood glucose, but exercise can be aerobic or anaerobic and these affect blood glucose in opposite ways. There has been research aiming to predict blood glucose by analysing previous recorded data from continuous glucose monitoring devices. This research applies a new approach with evolutionary computation to evolve a neural network, using neuro evolution, and the optimised neural network is then applied to predict and forecast blood glucose changes. In the comparison of accuracy, the results show that evolved neural network outperformed a back-propagation neural network in this task on forecasting CGM data. This can help people with diabetes to have a better idea about how their blood glucose is going to change before it occurs, so that hypo and hyper can be avoided. This can reduce diabetes complications and costs for the health service.