## 4 Results  
This section presents the results obtained from the evaluation of the trained machine learning (ML) models, using the Root Mean Square Error (RMSE) and the R-squared correlation coefficient metrics to measure the quality of the predictions. First, we address the quality of real-time predictions, presenting a comparison between algorithms and datasets. Additionally, we present the most relevant features used by the ML algorithm offering the best predictions. Moreover, we compare these real-time predictions with future ones based on 8, 16, and 24 h temporal windows. Regarding real-time predictions, Figure 11 presents a plot per trained model, indicating the RMSE associated with each dataset over both individual and aggregated approaches. We can observe two bars per user, corresponding to diastolic (lighter color) and systolic (darker color) predictions. It is interesting to note that Random Forest and Decision Tree present the same error for diastolic and systolic predictions since these algorithms are the only ones allowing multi-label training. The other requires training independent models for each blood pressure value. Focusing on the results, we can observe that the RMSE dramatically grows for users 2, 3 and 6 in SVR, BR, LR and PR algorithms. This situation is motivated by _underfitting_ during the training process, where the quality of the data was not enough to allow those algorithms to converge. Without considering these particular situations, the ML algorithms offer good results. Figure 12 presents a heat map containing those users who did not suffer underfitting during the training process, thus excluding users 2, 3, and 6 to visualize better the data previously introduced by Figure 11. This heat map indicates that users 1, 4, and 5 present better RMSE values than user seven and the general dataset. Additionally, Random Forest presents the best results in predicting both systolic and diastolic blood pressure values. Nevertheless, particular algorithms can independently predict either systolic or diastolic values better than Random Forest, offering the possibility to select which algorithms are more suitable for each user. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Parameter** & **Value** & **Description** \\ \hline Random Forest & max\_features & 72 & Number of features considered \\ \hline  & min\_samples\_split & No limit & Minimum number of samples to split a node \\ \hline  & max\_depth & No limit & Max depth in the tree \\ \hline  & n\_estimators & 189 & Number of trees in the forest \\ \hline Decision Tree & max\_features & 46 & Number of features considered \\ \hline  & min\_samples\_split & 54 & Minimum number of samples to split a node \\ \hline  & max\_depth & 25 & Maximum depth of the tree \\ \hline  & splitter & 189 & Strategy used to split a node \\ \hline Support Vector Regression & Kernel & Lineal & Type of kernel used by the algorithm \\ \hline  & C & 1 & Regularization parameter \\ \hline  & gamma & 2027 & Coefficient of the kernel \\ \hline Bayesian Ridge & n\_iter & 152 & Maximum number of iterations \\ \hline Linear regression & n\_jobs & 122 & Number of jobs used for the computation \\ \hline Polynomial regression & grade & 4 & Grade of the polynomial \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of the optimal values for the automatically selected parameters for each implemented machine learning (ML) algorithm. Once evaluated the algorithms in terms of the RMSE, Figure 13 presents their evaluation using the R-squared correlation coefficient. We can observe that several algorithms have similar correlations, being Random Forest the algorithm having the best results. The correlation values presented in this figure are calculated based on the relationship between the real blood pressure values and those predicted by the algorithms. This relationship is represented in Figure 14 for predictions using Random Forest over the general dataset and differentiating between systolic and diastolic values.