## 5 Methods  
Previous studies presented by some of the authors of this work showed that a proper time window to model glucose dynamics involves the last six hours [50]. Therefore, the data on the chosen features taken within that past timeframe are used as inputs. After the above-mentioned  
Figure 1: Correlation between features. preliminary considerations, and with the aim of building the proposed models, some parameters of the previously explained techniques need to be tuned. Thus, on the basis of the literature reviewed in Section 2.2. and after carrying out some preliminary estimations regarding the possible range of the tuning parameters, the following values for adjustment are considered, with the goal of finding the best fitting for each model. Gaussian Process with Radial Basis Function Kernel (GP-RBFK)  
* Function in R software: gaussprRadial
* Tuning parameter: sigma (radius of the RBFK)
* Values for tuning: 0.01, 0.05, 0.1, 0.5, 1 Multi-Layer Perceptron (MLP)
* Function in R software: mlp
* Tuning parameter: size (number of neurons in the hidden layer)
* Values for tuning: 10, 20, 30, 40, 50, 60 Support Vector Machines with Radial Basis Function Kernel (SVM)
* Function in R software: svmRadialCost
* Tuning parameter: Cost (the parameter Cost controls the trade-off between margin maximization and error minimization)
* Values for tuning: 1, 2, 5, 10, 15, 20 Bayesian Regularized Neural Networks (BRNN)
* Function in R software: brnn
* Tuning parameter: neurons (number of neurons)
* Values for tuning: 2, 3, 4, 5, 10 In order to generate the appropriate models, the following steps are followed:
* Transformation: The above-mentioned parameters should be properly transformed to become appropriate inputs of the considered models so that they become representative features in techniques such as NN. Some features could be the regularity with which each parameter is updated, its historical values, etc. Selecting variables, deleting outliers, transforming numerical variables, and dividing the set of data into train and test sets. * Normalization: After the normalization process, all the values are in a [0,1] interval. With this, we also transform the features in order to have a mean of zero and unit standard deviation. * Evaluating metric: The RMSE gives us an idea about a mean of the error achieved, and the R-squared explains the standard deviation of the fitting. The RMSE has been widely used in previous literature [16; 17; 18; 19; 20; 21; 22; 23; 24; 25] and it is, therefore, the preferred parameter for comparison purposes, being that it offers a single value that estimates the accuracy of the models under discussion. * PCA--Principal Components Analysis: With PCA, a new set of orthogonal coordinate axes is introduced, along which the sample data variance is maximized. This means that the other directions in which the variance is less than an assumed value are not important; these can, therefore, be removed from the training data set. By using PCA, it is (sometimes) possible to obtain lower values of RMSE, which, as previously mentioned, will be the evaluation metric. PCA is a very efficient way to approximate the data in a lower dimensionality and reveal some hidden and simplified structures/patterns that often underlie the data. * Validation method: 10-fold cross-validation and five repetitions over the training data set. In order to generate the different models and compare the previously described approaches, a software specifically prepared for such a purpose is used, namely the CARET package (Classification And REgression Training) [51] for data modeling of the R software. This package is a set of functions that attempt to streamline the process of creating predictive models. All the computation is done on a server with the following characteristics: Microprocessor AMD Ryzen Threadripper 1920X (12 cores, 24 threads), 128 Gb RAM 2666 MHz, 2 xGPU GeForce RTX 2070 with 8 Gb RAM DDR6 per unit, and an SSD Samsung 860 4 Tb.