title: [Use of an artificial neural network to predict the incidence of malaria in the city of Cantá, state of Roraima].
abstract: BACKGROUND
  Malaria is endemic in the Brazilian Amazon region, with different risks for each region. The City of Cantá, State of Roraima, presented one of the largest annual parasite indices in Brazil for the entire study period, with a value always greater than 50. The present study aimed to use an artificial neural network to predict the incidence of malaria in this city in order to assist health coordinators in planning and managing resources.
  
  
  METHODS
  Data were collected on the website of the Ministry of Health, SIVEP--Malaria between 2003 and 2009. An artificial neural network was structured with three neurons in the input layer, two intermediate layers and an output layer with one neuron. A sigmoid activation function was used. In training, the backpropagation method was used, with a learning rate of 0.05 and momentum of 0.01. The stopping criterion was to reach 20,000 cycles or a target of 0.001. The data from 2003 to 2008 were used for training and validation. The results were compared with those from a logistic regression model.
  
  
  RESULTS
  The results for all periods provided showed that the artificial neural network had a smaller mean square error and absolute error compared with the regression model for the year 2009.
  
  
  CONCLUSIONS
  The artificial neural network proved to be adequate for a malaria forecasting system in the city studied, determining smaller predictive values with absolute errors compared to the logistic regression model and the actual values.
authors.affiliation: Curso de Ciência da Computação, Universidade Federal de Roraima, Boa Vista, RR. guilherme.ufrr@gmail.com
authors.forename: Guilherme Bernardino da
authors.initials: GB
authors.lastname: Cunha
authors.forename: José Francisco
authors.initials: JF
authors.lastname: Luitgards-Moura
authors.forename: Eduardo Lázaro Martins
authors.initials: EL
authors.lastname: Naves
authors.forename: Adriano Oliveira
authors.initials: AO
authors.lastname: Andrade
authors.forename: Adriano Alves
authors.initials: AA
authors.lastname: Pereira
authors.forename: Selma Terezinha
authors.initials: ST
authors.lastname: Milagre
mesh_terms: D001938:Brazil / Q000453:epidemiology; D005544:Forecasting; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D008288:Malaria / Q000453:epidemiology*; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D013997:Time Factors