Redes neurais artificiais aplicadas à previsão da incidência de malária no estado de Roraima
The present work aims to create a prototype called SISPIMA - forecast system in the incidence of malaria, to generate estimates of the incidence of malaria in Roraima state in three different periods: short term (3 months), medium term (6 months) and long term (12 months). To develop the system, wer...
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| Formato: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2010 |
| País: | Brasil |
| Recursos: | Universidade Federal de Uberlândia (UFU) |
| Repositorio: | Repositório Institucional da UFU |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufu.br:123456789/14275 |
| Acesso em linha: | https://repositorio.ufu.br/handle/123456789/14275 |
| Access Level: | acceso abierto |
| Palavra-chave: | Redes neurais artificiais Previsão da incidência de malária Backpropagation Modelo ARIMA Suavização exponencial Redes neurais (Computação) Malária - Roraima Artificial neural network Forecasting of malaria ARIMA models Exponential smoothing CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
| Resumo: | The present work aims to create a prototype called SISPIMA - forecast system in the incidence of malaria, to generate estimates of the incidence of malaria in Roraima state in three different periods: short term (3 months), medium term (6 months) and long term (12 months). To develop the system, were employed techniques of artificial neural networks and time series analysis. The SISPIMA consists of four steps: collection and storage of data, preprocessing, training and predicting the incidence of malaria. Data were obtained through access to the site SIVEP-Malaria Health Ministry. These were filtered, normalized and classified by SISPIMA in the pre-processing before performing the training and prediction. For training and forecasting, used artificial neural networks. The architecture of artificial neural network used was the multilayer perceptron (MLP) with a variation of the backpropagation training algorithm, called of Resilient Propagation (RPROG). To validate the results and assess the performance and accuracy of the proposed system, we use the ARIMA model as a comparison because of its wide application in epidemiological time series forecasting. |
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