Dengue confirmed-cases prediction: A neural network model

Hani M. Aburas, B. Gultekin Cetiner, Murat Sari*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

61 Citations (Scopus)

Abstract

This research aims to predict the dengue confirmed-cases using Artificial Neural Networks (ANNs). Real data provided by Singaporean National Environment Agency (NEA) was used to model the behavior of dengue cases based on the physical parameters of mean temperature, mean relative humidity and total rainfall. The set of data recorded consists of 14,209 dengue reported confirmed-cases have been analyzed by using the ANNs. It has been produced very encouraging results in this study. The results showed that the four important features namely mean temperature, mean relative humidity, total rainfall and the total number of dengue confirmed-cases were very effective in predicting the number of dengue confirmed-cases. The ANNs have been found to be very effective processing systems for modelling and simulation in the dengue confirmed-cases data assessments. The proposed prediction model can be used world-wide and in any period of time since the approach does not use time information in building it.

Original languageEnglish
Pages (from-to)4256-4260
Number of pages5
JournalExpert Systems with Applications
Volume37
Issue number6
DOIs
Publication statusPublished - Jun 2010
Externally publishedYes

Keywords

  • Artificial Neural Network
  • Dengue
  • Modelling
  • Prediction
  • Simulation

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