Abstract
Nowadays wearable devices are very popular. The reason for that is the sudden reduction in pricing and the increase in functionalities. Healthcare services have been greatly benefiting from the emergence of these devices since they can collect vital signs and help healthcare professionals to easily monitor patients. Medical wellness, prevention, diagnosis, treatment and monitoring services are the main focus of Healthcare applications. Some companies have already invested in this market and we present some of them and their strategies. Furthermore, we also conducted a group interview with Altice Labs in order to better understand the critical points and challenges they encountered while developing and maintaining their service. With the purpose of comprehending users’ receptiveness to mHealth systems (mobile health systems which users wear - wearables) and their opinion about sharing data, we also created a questionnaire (which had 114 valid responses). Based on the research done we propose a different approach. In our product and service concept solution, which we share herein, we consider people of all ages to be targets for the product/service and, beyond that, we consider the use of machine learning techniques to extract knowledge from the information gathered. Finally, we discuss the advantages and drawbacks of this kind of system, showing our critical point of view. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

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The dropout of university students has been a factor of concern for educational institutions, affecting various aspects such as the institution’s reputation and funding and rankings. For this reason, it is essential to identify which students are at risk. In this study, algorithms based on decision trees and random forests are proposed to solve these problems using real data from 331 students from the University of Trásos-Montes and Alto Douro. In this work with these learning algorithms together with the training strategies, we managed to obtain an 89% forecast of students who may abandon their studies based on the evaluations of both semesters related to the first year and personal data. © 2021, Springer Nature Switzerland AG.

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