## 4 Discussion and Lessons Learnt
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**Category** & **Key Aspect** & **Description** & **Case Study** & **Sample** \\  & & & & & **References** \\ \hline Scalability & & Support for handling large amounts of data (efficient and effective collection, storage, management, and exploitation). & The current project is still in an initial stage, with no large-scale deployment. No scalability problems have collected for many years, scalability is a key issue to consider. & \\ \hline  & & Minimization of the number of false negatives (increasing recall). This is a key goal in healthcare, since false negatives and false positives have no similar costs in this area. The precision should also be suitable, as a high number of false positives would lead to false alarms, the performance of needles procedures, and increasing costs and discomfort for the patients. & The selected algorithms (SVM, random forest, MLP neural network, and GBA) have recall \textgreater{}90\%. & [39] \\
**Predictive model** & High recall and relatively high precision & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & \\ \hline \hline  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & \\  & & & & \\  & & & \\  & & & & \\ \hline \hline \end{tabular}
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Table 5: _Cont._model [19; 21]. Although converting continuous predictors to discrete variables (specifically binary variables) is not always recommended [20], the necessary transformation of data for privacy reasons in the field of health conditions the stage of data transformation. The reduction in the volume of data in the case study was from 230 variables to 28 due to the large number of variables provided by the inertial sensors and the clinical data. The variable reduction applied (based on the gain ratio of the variables) after the corresponding selection of the data for ethical and legal issues and the screening made by an expert was necessary to fulfil the 1 to 10 ratio between the variables and data. This key point allowed us to ensure that the predictive models work properly, maximizing the predictive power and avoiding overfitting. **Selection of the predictive model. ** Regarding the selection of the predictive model, after performing a parameter tuning of the seven selected algorithms and comparing the most suitable configuration of each of them (see Figure 5), it was concluded that the models that meet the established requirements regarding accuracy, precision, and recall for the case study were SVM, random forest, MLP neural networks, and GBA. Our results highlight the low number of false negatives achieved (high recall), a fundamental aspect in healthcare studies [39]. These results are in agreement with other investigations of a similar nature, using the same software (Weka), in terms of the accuracy, precision, recall, and F1 score with the SVM and random forest algorithms [36]. **Variability of the measures acquired. ** It has been detected that it is possible to assess the measurement capacity of our medical equipment in terms of the variability of the measures that it obtains. A _series_ variable identifies whether the data are relative to the first measure of each subject or to the second (which was performed consecutively).