## 5 Conclusions and Future Work  
Through a practical guide, the stages and particularities to consider for the application of ML techniques in the field of healthcare have been described, considering all the stages involved in the process. This procedure is shown through objective cervical functional assessment tests that use MoCap technology with inertial sensors and a predictive model whose goal is to estimate the presence of cervical pain from the data collected with the test. Four models (SVM, random forest, MLP neural network, and GBA) from the seven models initially generated obtained an accuracy and precision of more than 85% and a recall of more than 90% (i.e., the percentage of false negatives is smaller than 10%). The approach and the results obtained could help objectify diagnoses, improve test treatment efficacy, and save resources in healthcare systems. The procedure, which has been applied to data derived from a cervical assessment study for verification and evaluation, is also appropriate for any healthcare field regardless of the origin of the data. It can be useful, for example, in gait studies [82], balance studies [84], cardiac failure studies [85], the prediction of events [86], fertility tests [87], etc. Despite the great usefulness of ML in the field of healthcare, some limitations have been detected in this field. First, a major limitation is how to achieve a suitable flow of data from health centers and hospitals, as well as the accessibility (in relation to privacy policies and authorizations) [21], gathering, and integration of the data [3]. If a global information collaboration policy were established between hospitals [6; 88; 69], the problem of access to information could be solved, and it would be possible to share more data and feed the predictive models applied to the field of healthcare more efficiently. The explainability of predictions [89; 90] is an important issue in a health care domain, as it could increase the trust in the ML systems (for both clinicians and patients) and even lead to the acquisition of new knowledge; however, more research on how to achieve explainability while considering the potential trade-off with accuracy must be performed, especially in the health domain. Regarding the limitations of the conclusions obtained with this case study, once the model is in the exploitation stage it would be advisable to carry out an external validation to verify the viability of the model in terms of geography, temporality, etc. [30; 77]. Regarding the data sample used in our case study, its size has been large enough to obtain good results, but it would be relevant to see the impact of increasing it, as new data about patients becomes available, to enable a continuous learning process that could lead to better results over time. Besides, a study is currently being conducted to check the accuracy of the proposed models with a sample of non-collaborating patients. Concerning the target variable (presence of cervical pain), which is a binary variable, it could be defined in a more granular way considering not only the presence of pain but also its intensity (as a continuous variable or as a discrete variable with several pain degrees). The problem with pain intensity is that pain scales are highly dependent on the subjectivity of the patient, and this issue could be further exacerbated with non-collaborating subjects. However, as a future goal of our research, it could be useful to tackle this issue and introduce some statistical techniques, such as the numerical measurement z-score, to normalize the subjective values from pain intensity scales (e.g., the Visual Analogue Scale) provided by the patients. The z-score could help to reduce the bias of patients, allowing us to include pain intensity as a target variable in our proposal.