Autism Spectrum Disorder (ASD) is a common but complex disorder to diagnose since there are no imaging or blood tests that can detect ASD. Several techniques can be used, such as diagnostic scales that contain specific questionnaires formulated by specialists that serve as a guide in the diagnostic process. In this paper, Machine Learning (ML) was applied on three public databases containing AQ-10 test results for adults, adolescents, and children; as well as other characteristics that could influence the diagnosis of ASD. Experiments were carried out on the databases to list which attributes would be truly relevant for the diagnosis of ASD using ML, which could be of great value for medical students or residents, and for physicians who are not specialists in ASD. The experiments have shown that it is possible to reduce the number of attributes to only 5 while maintaining an Accuracy above 0.9. In the other Database to maintain the same level of Accuracy, the fewer attribute numbers were 7. The Support Vector Machine stood out from the others algorithms used in this paper, obtaining superior results in all scenarios.

With the growing increase in the population’s life expectancy, it isnatural that more and more aged people make use of technologicaltools. Those tools need to meet the necessities of this public that, ingeneral, has more difficulties to deal with technologies. This paperdescribes the development process of a system built for a supporthome for elderly people and volunteers with little experience inusing computerized systems. It is a technological support tool thehelps the institution in a safe and reliable way. Usability problemsfor seniors have been minimized with specific guidelines.

This work presents an interface for a Skin Cancer Sublanguage,which uses a non-SQL oriented database technology, called MongoDB,implementing the concept of Natural Language Interfaceto Databases (NLIDB). When compared to relational databases,No-SQL (Not Only SQL) mechanism is more scalable, it providessuperior performance and addresses several issues that a relationalmodel is not designed to solve. All obtained words and their variationsare labeled and stored in a MongoDB database, thus allowingcustomized queries to search and join morphemes. A dictionarywill be used to build dialogs, narratives and diagnoses in a SeriousGame with educational purposes that in addition obtains accurateanswers to the project’s database.