|Insulin therapy for type 1 diabetic patients often results in high variability in blood glucose concentration and may cause hyper-and hypoglycemia. Insulin is known with its time lag action, there is a time lag between insulin infusion and its effect on lowering blood glucose (BG) concentration. Designing a closed loop control system for blood glucose concentration needs a good performed predictor, especially for long prediction horizons. Neural networks are widely used in blood glucose prediction with good performance, especially for short prediction horizons. Improving prediction performance of feed forward neural network (FFNN)for higher prediction horizon values is an aim of many re-search topics. In this paper, we propose an algorithm that uses whale optimiza-tion algorithm (WOA) in training feed forward neural networks that are already used before in BG concentration prediction. The results show that, WOA in BG predictor training reduces RMSE and Normalized prediction error (NPE) for all prediction horizons (PHs). The performance enhancement increases with increase of prediction horizon. FIT value of the predicted BG after 60 minutes is also increased from 47.6% to 63.1% when WOA is added in FFNN training. The performance of our prediction model is comparable with other neural net-work referenced prediction models. We developed a FFNN predictor that is able to predict 60 minutes ahead.|
*** Title, author list and abstract as seen in the Camera-Ready version of the paper that was provided to Conference Committee. Small changes that may have occurred during processing by Springer may not appear in this window.