## 6 Results and Discussion  
The four techniques described in the previous sections were used to train the glucose modeling to determine the optimal configuration of their hyperparameters. Using the above-mentioned R-package called CARET, the implementation of the four techniques enabled us to adjust their tuning values. Hereby, each technique model was trained by using the above-mentioned parameter values so that the one that achieved the best results--according to the chosen evaluation metric (that is, the RMSE)--was selected. The RMSE formulation appears in Equation (1). This metric shows the error expressing the quantity of glucose concentration (mg/dL) that deviated when predicting (with \(y_{i}\) as predicted values and \(y_{i}\) as real values, considering n elements). The customary method of performing parameter-streamlining has been a grid search, which is just a comprehensive way of looking through a manually-determined subset of the parameter space of a learning technique. A grid search calculation must be guided by some presentation metric, ordinarily estimated by cross-validation on the training set. With the aim of better examining the uncertainty of each model, their coefficient of variation (CV-RMSE) is also calculated. This coefficient is the named RMSE and is divided by the mean of the glycemia \(\overline{y}\) (which is the output variable) related to the test set (see Equation (2)), resulting in a percentage of error and not just a number in general terms. \[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{1}\]  
\[CV-RMSE=\frac{RMSE}{\overline{y}} \tag{2}\]  
By considering the 5-times 10-fold cross-validation, the RMSE for the four techniques was obtained, and the results are depicted in Figure 2. As can be observed, for every situation, we find significant differences between each pair of algorithms. It is possible to appreciate the differences between the results for each technique, but in order to perform statistical analysis, a pairwise Wilcoxon post-hoc analysis for a Friedman test was performed (\(\alpha\) = 0.99) to check if there were any differences in their outcomes. The test revealed that there is a significant difference between the groups (\(p\)-value always below \(1\times 10^{-5}\)). Specifically, a comparison of the accuracy obtained for the different techniques is present in Figure 3, and the best tuning option is indicated in Table 2.  
As can be seen, BRNN yields the best result when compared to the other tested techniques, based on the named RMSE metric, for a configuration of two neurons (RMSE = 14.03 mg/dL and R-squared = 0.83). This technique goes beyond the limits of the neural networks in that it is more robust than usual BP nets. In addition, the number of neurons is usually considerably smaller than a BP neural net's weights [21]. Nevertheless, SVM could also be a good option as its outcome is only slightly worse than that obtained for BRNN (RMSE = 15.31 mg/dL and R-squared = 0.79). In any case, we can state that the results show that BRNN would be the best technique due to its accuracy and good performance. On the other hand, the worst performance is that achieved with the GP technique, with an R-squared under 0.65 and an RMSE of the glycemia prediction of 21.64 mg/dL, which would be barely permissible; this reaffirms the idea that GP loses efficiency when the number of features increases [27]. In this sense, it is necessary to bear in mind that, for example, such an error would lead, in medical terms, to an estimated glucose level of 90 mg/dL (euglycemia) when the real value is around 70 mg/dL (hypoglycemia). However, as previously stated, with an R-squared of 0.83 and an RMSE of 14.03 mg/dL, BRNN exhibits an acceptable level of representativeness; therefore, we can conclude that this technique, with this configuration, would be the better option to build the modeling core of a system to reliably model glycemia dynamics.