## 6 Conclusions and Future Work  
This work presents the analysis of a dataset containing calls received by the Portuguese National Health Contact Center and a comparison of different Machine Learning approaches to the task of classifying these calls according to their contact reason. To help further understand the problem, the data was characterised, with its distribution regarding the classes of health problems and the customer gender and age and living district being reported. The service seems to have a great impact in child care and young adults, mainly on coastal districts. The contact reason of the reported situation serves to help in selecting the pathway algorithm to be followed by the attendant to reach the appropriate referral between self-care, clinical assessment at primary care unit or at a hospital emergency if not transferred to the INEM. The pathway is to be chosen from 59 options so Artificial Intelligence techniques are applied to accelerate the finding of the best choice. A comparative analysis among the best options according to the literature was presented and, on the basis of three years data, different configurations of training sets were tested. The conclusion is that date proximity along with larger training data are relevant for producing models with better performance. The unbalanced nature of the classes normally impacts the generation of the models; this was confirmed for this problem when analysing performance and frequency. Anotherissue found in the study is the great similarity among symptoms of some pathways, such as \"Cough\" and \"Flu syndrome\", having a clear negative impact in the performance due to the difficulty to generalise the fine distinctions among these situations. Moreover, it was shown that current techniques aiming at explaining ML models decisions may be applied by providing users with an explanation of the system decision, an essential feature for AI applications in the health domain. It is also important to note that the ground truth was considered the right decision, assuming that the attendant is always correct regarding the chosen algorithm, a situation that was questioned during the process of meta evaluation. The presented error analysis enables to stress the importance of having a decision support system to help the attending nurses decide the best clinical pathway when in doubt. Regarding the comparison of different classification methods, the best results were obtained with CNN, with an F1 of 0.782 against a maximum of 0.768 obtained with SVM. It is known, however, that the computational cost for training DNNs are much higher than the cost of using SVM. Moreover, the experiments showed that, although both ML algorithms give an answer within the reasonable time for having the system running in real time, the SVM model classification time is higher especially for bigger models. There are issues regarding these choices and their associated carbon footprint [35; 36]. No specific carbon footprint measurements were done in this study but it is known that there is a large difference in resources consumption between these two approaches. Considering that the application of these solutions on a daily practice will require a periodical re-training, that is a relevant factor indicating the one year SVM model as a fair alternative, if a 1.4% reduction in performance (accuracy, precision, recall and F1) is not crucial. As future work we intend to investigate other ML algorithms, namely a boosting ensemble approach like Gradient Tree Boosting [37], since these models generally provide top accuracies if the parameters are set correctly, and update the models with the calls from 2020 and 2021 when new clinical pathways related to COVID were designed. On a different line of research, we intend to study the consistency between the clinical pathway final referrals and the follow-up screening at the hospital emergency or primary health care unit.