@InProceedings{LopesOliBriOliRap:2021:EsNíOb,
               author = "Lopes, Leonardo Ferreira and Oliveira, Adonias Caetano de and 
                         Brito, Rhyan Ximenes de and Oliveira, Saulo Anderson Freitas de 
                         and Raposo Neto, Luiz Torres",
          affiliation = "IFCE and IFCE and IFCE and IFCE and IFCE",
                title = "Estimativa dos N{\'{\i}}veis de Obesidade com Base em 
                         H{\'a}bitos Alimentares e Condi{\c{c}}{\~a}o F{\'{\i}}sica 
                         Atrav{\'e}s de T{\'e}cnicas de Aprendizado de M{\'a}quina",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Obesidade, M{\'a}quinas de Vetores de Suporte, Floresta 
                         Aleat{\'o}ria.",
             abstract = "Obesity is a chronic disease that affects several countries, 
                         causing damage such as respiratory and locomotor difficulties, 
                         metabolic changes, cardiovascular problems, and even death, in the 
                         extreme case. In this perspective, this initial study aims to 
                         evaluate the classifiers' performance, namely, Random Forest and 
                         Support Vector Machine, when estimating obesity levels, with data 
                         from the set 'Estimation of obesity levels based on eating habits 
                         and physical condition Data Set'. Under cross-validation and 
                         Hold-Out, preliminary results indicate an average accuracy with 
                         SVM around 87.84% and RF around 95.18%. Furthermore, we noticed 
                         that our approach recognizes overweight and obesity cases better, 
                         while such cases, in the latest work, are more critically 
                         neglected, misclassifying the most severe degree of obesity. Thus, 
                         comparing our results with related works, we concluded that the 
                         models studied are suitable to the problem, given the achieved 
                         results.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "pt",
                  ibi = "8JMKD3MGPEW34M/45E96QL",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45E96QL",
           targetfile = "versao_final.pdf",
        urlaccessdate = "2024, May 06"
}