Using Machine Learning to identify profiles of individuals with depression

  • Carlos D. Maia Pontifícia Universidade Católica de Minas Gerais
  • Cristiane N. Nobre Pontifícia Universidade Católica de Minas Gerais
  • Marco Paulo S. Gomes Pontifícia Universidade Católica de Minas Gerais
  • Luis E. Zárate Pontifícia Universidade Católica de Minas Gerais


Depression is a major public health problem in Brazil, affecting millions of individuals each year. While the prevalence of depression in Brazil has been well-documented, there is still a need for more accurate and timely predictions of depression trends to improve treatment and prevention strategies. In this study, we explored the potential of machine learning algorithms to forecast depression trends in Brazil using data from the National Health Survey conducted by the Brazilian Institute of Geography and Statistics. We compared the performance of various machine learning models in depression trends, including decision trees, random forests, support vector machines, and neural networks. Additionally, we aimed to identify key risk factors for depression trends in Brazil, including age, gender, income, education, and marital status. These findings have important implications for public health policies and mental healthcare in Brazil. Our study provides insights into the use of machine learning algorithms to predict and prevent depression trends and highlights the potential of data-driven approaches to improve mental health outcomes in Brazil.

Palavras-chave: Machine Learning, Health, Depression


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MAIA, Carlos D.; NOBRE, Cristiane N.; GOMES, Marco Paulo S.; ZÁRATE, Luis E.. Using Machine Learning to identify profiles of individuals with depression. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 11. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 105-112. ISSN 2763-8944. DOI: