Aligning the Goals Hybrid Model for the Diagnosis of Mental Health Quality

The social distancing imposed by the COVID-19 pandemic has been described as the "greatest psychological experiment in the world”. It has tested the human capacity to extract meaning from suffering and challenged individuals and society in Brazil and abroad to promote cohesion that cushions the impact of borderline experiences on mental life. In this context, a survey was conducted with teachers, administrative technicians, and outsourced employees at the Federal Institute of Piauí (IFPI). This educational institution offers professional and technological education in Piauí, Brazil. This study proposes a system for the early diagnosis of health quality during social distancing in the years 2020 and 2021, over the COVID-19 pandemic, combining multi-criteria decision support methodology, the Analytic Hierarchy Process (AHP) with machine learning algorithms (Random Forest, logistic regression, and Naïve Bayes). The hybrid approach of the machine learning algorithm with the AHP multi-criteria decision method with geometric mean accurately obtained a classification that stood out the most in the characteristics' performance concerning emotions and feelings. In 2020, the situation was reported as the SAME AS BEFORE, in which the hybrid AHP with Geographical Average with the machine learning Random Forest algorithm stands out, highlighting the atypical situation in the quality of life of the interviewees and the timely manner in which they realized that their mental health remained unchanged. After that, in 2021, the situation was reported as WORSE THAN BEFORE, in which the hybrid AHP with geometric mean with the machine learning Random Forest algorithm provided an absolute result. © 2023 by the authors.

Application of Machine Learning to Infer Symptoms and Risk Factors of Covid-19

This article presents a model that aims to identify with Machine Learning (ML) technics the main symptoms and risk factors affected in patients with Coronavirus Covid-19, registered in the database of epidemiological surveillance of state and municipal information in Brazil. The concept behind ML is the ability to learn and reason. Its application can optimize and make the treatment and care process more accurate for the cases diagnosed with the Covid-19, also known as SARS-CoV-2, adjusting the medical data recorded concerning the disease and reducing the number of symptoms and risk factors, denoting an efficient form of attribute engineering, providing those involved with the clinical observation of a minor sign. We propose an approach structured in the composition of Machine Learning algorithms, aiming to discover knowledge and concepts followed by the refinement of the results. In this article, the proposed model is presented, and a shorter trail of symptomatic observations from Covid-19 are provided. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.