## 4 Results
We can observe that the trend of the dots is almost linear in the systolic predictions, while the diastolic predictions are a little sparser. Figure 11: Histograms indicating the Root Mean Square Error (RMSE) of each trained model for the real-time approach. Lighter colors represent the diastolic blood pressure while darker bars indicate systolic values. Figure 12: Heatmap indicating the RMSE error for each trained model. To better understand the relevance that individual features have over the predictive process, Figure 15 presents the list of the most important features considered by the RF classifier, corresponding to the algorithm presenting better results for real-time predictions. This representation results from the evaluation of the general dataset containing data from all seven subjects. As can be seen, the training process has determined that the most relevant feature corresponds to the users' age. In this regard, the risk of suffering high blood pressure increases when people get older. Additionally, the second most relevant feature is the user identifier, which is evident considering that we have a hypertensive patient between the subjects. Following these features, we can see three medicines, where Lercanidipine is an antihypertensive drug, Sertraline is an antidepressant, and Zolpidem is used to treat sleeping problems. Nevertheless, these medicines were consumed by the hypertensive subject, thus increasing the relevancy of these features. Figure 14: Comparison between real and predicted values for systolic and diastolic predictions using Random Forest over the general dataset. Figure 13: R-squared correlation coefficient for real-time predictions. Furthermore, this figure highlights two diet survey questions, which correspond to the type of meat (or the absence of meat consumption), and the number of fish and seafood consumed by the subject per week. It is relevant to note that a transition from red meat to white meat or fish has proven to reduce high blood pressure. Additionally, people following plant-based diets tend to have lower blood pressure. After these features, we can observe the importance of physical activity, where the number of daily steps, the duration of vigorous physical activity, and the number of days per week going out for a walk impact tension values. Moreover, the measurement instant during the day is also relevant since blood pressure increases during the day until the afternoon, decreasing in the evening and night. The heart rate is also related to blood pressure (a high heart rate presents a risk for hypertension), while people with an elevated weight have a higher risk of suffering hypertension. Finally, we can observe multiple features regarding the weather. Although these features do not have a high impact on the system, they are interesting to consider. Finally, pollution features have not substantially impacted this analysis since the users tended to remain in the same cities, not generating variability in the collected data. Nevertheless, these features may intervene in longer temporal acquisition windows and with the use of different users or algorithms. After introducing the results of real-time predictions, we compare in Figure 16 the RMSE error between real-time and future predictions using Random Forest, considering future temporal windows of 8 h, 16 h, and 24 h. We have selected Random Forest for the comparison since it is the algorithm that offers the best results in real-time predictions. We can observe that the errors in the predictions generally increase when we use greater temporal windows, although the differences are acceptable. However, it highlights a considerable increase in the error when evaluating the general dataset. This situation is motivated by the great variability of data contained in this dataset, impacted by the uncertainty and future variations. Figure 15: Feature importance after the application of Random Forest over the general dataset.