## Relevant Literature
### Approaches to a Modeling Core
BRNN has a relationship with Bayesian parameter estimation, a classical statistical concept, and they also have a relationship with regularization concepts, e.g., ridge regression [23]. The concept of BRNN is that network parameters/weights should be treated as random variables that obey a theoretical distribution. The design of the distribution is such that less complex models are favored, i.e., models that produce a smooth fit. Having observed the data, evaluation of the posterior weight distribution takes place and this allows for the computation of the network prediction. These predictions will be reflective of both the smoothness aspect previously introduced and the fitness accuracy introduced through data observation. Other similar techniques, such as Gaussian Processes (GPs) with Radial Basis Function Kernels (RBF) [24], which allow a general uniformity and a non-limited number of basic functions, are rarely used, although some approaches have exploited this technique with promising results [25]. GPs have also been considered in a recent work [26] exploring the possibility of automatic insulin delivery, resulting in a reduction of hypoglycemia events. GPs represent a nonparametric methodology centered on modeling observable responses from various training data points (function values) as multivariate normal random variables [27]. An assumption is made of a priori distribution for such function values, which will guarantee the function's smoothness properties. To be specific, there will be a high correlation between the two function values when there is closeness (in the sense of Euclidean distance) between the corresponding input vectors and if they decay as they diverge. Posterior distribution of an as yet unpredicted function value may be derived from the employment of an assumed distribution through the application of simple probability manipulation. Support Vector Machines (SVM) are dual learning algorithms that process examples by solely computing their dot-products. These dot-products between feature vectors can be efficiently computed through a kernel function without iterating over all the corresponding features. Given the kernel function, the SVM learner tries to find a hyperplane that separates the positive from the negative examples and at the same time maximizes the separation (margin) between them. SVMs are known to be resilient to over-fitting and to have good generalization performance due to the max-margin criterion used during optimization. Furthermore, while the MLP solution may only be a local optimum, the SVM is guaranteed to converge to a global optimum due to the corresponding convex optimization formulation. A good attempt can be seen in [28], where the authors tried to predict hypoglycemia by considering electrocardiograms (ECG). By using a Fuzzy SVM method based on data collected from electroencephalogram (EEG), they found that almost 75% of low glucose levels were successfully predicted. Another SVM-based approach for hypoglycemia detection was shown in [29], which used a smart band, heart-rate monitoring, and a consideration of the galvanic skin response along with skin and air temperatures. Unfortunately, the results were limited by the size and nature of the dataset. SVM is centered on the employment of high-dimensional feature spaces (created through transformational original variables) and applying a penalty to the resulting complexity employing a penalty term inserted in the error function [30]. It is important to name some other approaches like deep learning. Although some deep learning models have recently demonstrated their capability to forecast glucose levels with an accuracy of RMSE = 9.38 \(\pm\) 0.71 mg/dL over a 30-min horizon [31], in our approach, we are not dealing with a large enough amount of data to apply this technique, but it is indeed an important approach to bear in mind according to the possibilities that it offers [32].