Gestational diabetes mellitus (GDM) is a public health problem. Along with changes in eating habits, increased purchasing power, and climate change, among others, the number of women with gestational diabetes complicated by pregnancy is increasing. GDM generates problems for the mother and for the baby. Therefore, early diagnosis is important to indicate adequate medical follow-up and treatment in a timely manner. In this context, we present a hybrid methodology of a specialized system structured in the Bayesian networks, the multicriteria approach of decision support, and artificial intelligence. In such a methodology, input parameters are proposed in order to support the early diagnosis of GDM, based on the symptoms of diseases that manifest in concomitance or that develop due to the favorable environment caused by the evolution of undiagnosed diabetes. The diseases and symptoms studied were extracted from the medical literature. The diseases were weighted using the Bayesian networks, based on data from the Health Maintenance Organization with coverage in 11 Brazilian states. The weights of the symptoms were tabulated according to the analysis of medical specialists, organized by the multicriteria methodology, applying multiattribute utility theory (MAUT) methods, in particular, MACBETH, by using the Hiview computational tool. Finally, the information was structured in the knowledge base of a specialist system, made in Expert SINTA software.

Autism Spectrum Disorder is a mental disorder that afflicts millions of people worldwide. It is estimated that one in 160 children has traces of autism, with five times the higher prevalence in boys. The protocols for detecting symptoms are diverse. However, the following are among the most used: the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5), of the American Psychiatric Association; the Revised Autistic Diagnostic Observation Schedule (ADOS-R); the Autistic Diagnostic Interview (ADI); and the International Classification of Diseases, 10th edition (ICD-10), published by the World Health Organization (WHO) and adopted in Brazil by the Unified Health System (SUS). The application of machine learning models helps make the diagnostic process of Autism Spectrum Disorder more precise, reducing, in many cases, the number of criteria necessary for evaluation, denoting a form of attribute engineering (feature engineering) efficiency. This work proposes a hybrid approach based on machine learning algorithms’ composition to discover knowledge and concepts associated with the multicriteria method of decision support based on Verbal Decision Analysis to refine the results. Therefore, the study has the general objective of evaluating how the mentioned hybrid methodology proposal can make the protocol derived from ICD-10 more efficient, providing agility to diagnosing Autism Spectrum Disorder by observing a minor symptom. The study database covers thousands of cases of people who, once diagnosed, obtained government assistance in Brazil.

Psychological disorders have kept away and incapacitated professionals in different sectors of activities. The most serious problems may be associated with various types of pathologies; however, it appears, more often, as psychotic disorders, mood disorders, anxiety disorders, antisocial personality, multiple personality and addiction, causing a micro level damage to the individual and his/her family and in a macro level to the production system and the country welfare. The lack of early diagnosis has provided reactive measures, and sometimes very late, when the professional is already showing psychological signs of incapacity to work. This study aims to help the early diagnosis of psychological disorders with a hybrid proposal of an expert system that is integrated to structured methodologies in decision support (Multi-Criteria Decision Analysis - MCDA) and knowledge structured representations into production rules and probabilities (Artificial Intelligence - AI).

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Organizations are increasingly investing in Distributed Software Development (DSD) over the years. A typical decision-making problem in the distributed scenario consists of deciding which team should be allocated each task. That decision takes into account a relative degree of subjectivity. That setting is suitable for applying Verbal Decision Analysis (VDA). This paper introduces an approach to support the allocation of tasks to distributed units in DSD projects, structured on the hybridisation of methods of Verbal Decision Analysis for classification and rank ordering applied to influencing factors and executing units. Firstly, a review of the literature was conducted aiming to identify the approaches to support the allocation of tasks in DSD contexts. Then, an approach was developed by applying VDA-based methods for classification and ordering. Bibliographic research and the application of surveys with professionals allowed identifying and characterising the main elements that influence task assignment in DSD projects. Afterwards, experiences were carried out in five real-world companies. In the end, the proposed approach has been submitted to the evaluation by the professionals of the participating companies and by some project management experts. The proposed approach comprises a workflow containing responsible actors and descriptions of the activities. Automated tools are also employed in automating the implementation of the approach. After applying the approach in five companies, task assignment recommendations are presented in groups for each company, according to the task type, i.e., requirements, architecture, coding, and testing, ranging from the most to the least preferable office. Results of the experiences and evaluations held during this work present evidence that the proposed approach is flexible, adaptable, and easy to understand and to use. Moreover, it helps to reduce decision subjectivity and to think of new aspects, supporting the task allocation process in DSD.