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This article compares ten recently proposed neural networks and proposes twoensemble neural network-based models for blood glucose prediction. All of themare tested under the same dataset, preprocessing workflow, and tools using theOhioT1DM Dataset at three different prediction horizons: 30, 60, and 120minutes. We compare their performance using the most common metrics in bloodglucose prediction and rank the best-performing ones using three methodsdevised for the statistical comparison of the performance of multiplealgorithms: scmamp, model confidence set, and superior predictive ability. Ouranalysis highlights those models with the highest probability of being the bestpredictors, estimates the increase in error of the models that perform morepoorly with respect to the best ones, and provides a guide for their use inclinical practice.

A Critical Review of the state-of-the-art on Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes