author = "Martins, Fl{\'a}via de Toledo and Dutra, Luciano Vieira and 
                         Pantale{\~a}o, Eliana and Sandri, Sandra and Freitas, Corina da 
                         Costa and Guimar{\~a}es, Ricardo Jos{\'e} de Paula Souza e",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Mapeamento do risco da esquistossomose em Minas Gerais usando k-NN 
                         e Žarvore de decis?ao",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "5912--5918",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Of all the parasitic diseases that affect humans, schistosomiasis 
                         is one of the most widespread. Considered a serious public health 
                         problem, the disease affects thousands of people in Brazil. Since 
                         the implementation of schistosomiasis control program in the state 
                         of Minas Gerais, stock control and surveillance have been 
                         conducted. To contribute to the control and mapping of endemic 
                         areas, the aim of this study is to obtain thematic maps showing 
                         the risk factor for schistosomiasis mansoni in Minas Gerais. 
                         Schistosomiasis is a disease caused by a worm that uses a snail as 
                         intermediary host. The worm uses the water to go from the snail to 
                         humans. Several variables can contribute for a high risk of a 
                         population contracting the disease. In this study, this risk is 
                         evaluated from climate, socioeconomic and remote sensing 
                         variables, which include MODIS and SRTM data. In this work, two 
                         pattern recognition techniques were used to generate two risk 
                         maps, with several parameter configurations. The first one is 
                         decision trees, for which a total of 19 classifications were 
                         generated. The second one technique is the nearest neighbour 
                         classification. For this method, only the number of neighbours 
                         varied, and 11 classifications were generated. Results showed a 
                         better result for the decistion trees in most part of the tests.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "1220",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4EQ8",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3JM4EQ8",
           targetfile = "p1220.pdf",
                 type = "Sa{\'u}de",
        urlaccessdate = "29 nov. 2020"