Use of an artificial neural network to predict the incidence of malaria in the city of Cantá, state of Roraima

Revista da Sociedade Brasileira de Medicina Tropical

Universidade Federal de Uberlândia, Uberlândia¹  Andrade, Adriano Oliveira¹  Milagre, Selma Terezinha¹  Luitgards-Moura, José Francisco¹  Pereira, Adriano Alves¹  Universidade Federal de Roraima, Boa Vista¹  Cunha, Guilherme Bernardino da¹  Naves, Eduardo Lázaro Martins¹ 

关键词: Prediction;    Malaria;    Artificial neural network;    Canta;    State of Roraima;    Logistic regression;   

DOI  :  10.1590/S0037-86822010000500019

学科分类：农业科学（综合）

来源: Sociedade Brasileira de Medicina Tropical

【 摘 要 】

INTRODUCTION: 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.

【 授权许可】

Unknown