  This article compares ten recently proposed neural networks and proposes two
ensemble neural network-based models for blood glucose prediction. All of them
are tested under the same dataset, preprocessing workflow, and tools using the
OhioT1DM Dataset at three different prediction horizons: 30, 60, and 120
minutes. We compare their performance using the most common metrics in blood
glucose prediction and rank the best-performing ones using three methods
devised for the statistical comparison of the performance of multiple
algorithms: scmamp, model confidence set, and superior predictive ability. Our
analysis highlights those models with the highest probability of being the best
predictors, estimates the increase in error of the models that perform more
poorly with respect to the best ones, and provides a guide for their use in
clinical practice.
