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

• Published in: Sustainability Vol. 15; no. 7; p. 5938
• Main Authors: Costa, Wagner Silva, Pinheiro, Plácido R, dos Santos, Nádia M, Cabral, Lucídio dos A. F
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Algorithms; Analytic hierarchy process; Brazil; Coronaviruses; COVID-19; Cushions; Data mining; Diagnosis; Disease control; Education; Employees; Epidemics; Learning algorithms; Loneliness; Machine learning; Mental disorders; Mental health; Mental illness; Multiple criterion; Pandemics; Psychiatric research; Psychological aspects; Quality of life; Questionnaires; Regression analysis; Social distancing; Students; Surveys; Sustainability; Teaching; Technicians; United Kingdom; Brazil; United Kingdom
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su15075938

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.