Automatic Prediction System of Dengue Haemorrhagic-Fever Outbreak Risk by Using Entropy and Artificial Neural Network

Rachata, Napa; Charoenkwan, Phasit; Yooyativong, Thongchai; Chamnongthal, K.; Lursinsap, Chidchanok; Higuchi, Kohji

doi:10.1109/iscit.2008.4700184

Cited by 31 publications

(20 citation statements)

References 3 publications

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“…Another research by Rachata et al (2008) also proposed NN and entropy model for predicting dengue hemorrhagic fever outbreak and the result demonstrated that it can achieve up to 85.92% of accuracy and concluded that the result is going better when using entropy transformation. Yusof (2011) develop a prediction model that incorporates LS-SVM and NN model in predicting dengue outbreak and the results demonstrated that LS-SVM produce the best result compare to NN in terms of predicting accuracy and computational time.…”

Section: Previous Workmentioning

confidence: 99%

“…Aburas et al (2010) Predict dengue cases on mean temperature, Neural network mean relative humidity, total rainfall and total number of dengue cases Cen and Wang (2008) Predict the lung cancer by introduces MultiHybrid Genetic Algorithm-BP Neural Networks species Co-evolution Genetic Algorithm and Simulated Annealing algorithm. Rachata et al (2008) Propose …”

Section: Previous Workmentioning

confidence: 99%

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Performance of Hybrid GANN in Comparison with Other Standalone Models on Dengue Outbreak Prediction

Husin¹,

Mustapha²,

Sulaiman³

et al. 2016

Journal of Computer Science

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Early prediction of diseases especially dengue fever in the case of Malaysia, is very crucial to enable health authorities to develop response strategies and context preventive intervention programs such as awareness campaigns for the high risk population before an outbreak occurs. Some of the deficiencies in dengue epidemiology are insufficient awareness on the parameter as well as the combination among them. Most of the studies on dengue prediction use standalone models which face problem of finding the appropriate parameter since they need to apply try and error approach. The aim of this paper is to conduct experiments for determining the best network structure that has effective variable and fitting parameters in predicting the spread of the dengue outbreak. Four model structures were designed in order to attain optimum prediction performance. The best model structure was selected as predicting model to solve the time series prediction of dengue. The result showed that neighboring location of dengue cases was very effective in predicting the dengue outbreak and it is proven that the hybrid Genetic Algorithm and Neural Network (GANN) model significantly outperforms standalone models namely regression and Neural Network (NN).

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Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Performance of Hybrid GANN in Comparison with Other Standalone Models on Dengue Outbreak Prediction

Husin¹,

Mustapha²,

Sulaiman³

et al. 2016

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…This research talk about forecasting of patient infected with dengue fever or not infected with dengue fever by comparing two statistic methods, ARMAX and NARMAX. Napa Rachata, et al [10] Predicting Dengue Haemorrhagic Fever outbreak is obviously urgent in order to control and prevent a widespread of the fever in advance. However, the prediction of Dengue Haemorrhagic Fever outbreak needs the analysis from experts which is inconvenient and costly.…”

Section: Related Workmentioning

confidence: 99%

A Prediction System of Dengue Fever Using Monte Carlo Method

Roziqin

Basuki

Harsono

2016

emitter

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Dengue fever is an acute disease that clinically can cause death because there is no prediction system to estimate dengue fever cases so it resulted in the growing of dengue fever cases every year. Original data gathering in Jember area that uses technique of partial data gathering has caused data missing. To make this secondary data can be processed in prediction stage there is need to conduct missing imputation by using Monte Carlo method with four different randomization method, followed by data normality test with chisquare, then continued to regression stage. We use MSE (Mean Square Error) to measure prediction error. The smallest MSE result of regression is the best regression model for prediction.

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“…The external factors such as temperature, relative humidity, and rainfall are considered during information extraction. To predict the risk of Dengue Hemorrhagic-Fever outbreak a supervised Neural Network is used and good accuracy was achieved [9].…”

Section: Literature Surveymentioning

confidence: 99%

Classification and Prediction Model using Hybrid Technique for Medical Datasets

Raghavendra¹,

Indiramma²

2015

IJCA

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For processing of large amount of data numerous techniques are used. Data Mining is one of the technique that is used most often. To process these data, Data mining combines traditional data analysis with sophisticated algorithms. Medical data mining is an important area of Data Mining and considered as one of the important research field due to its application in healthcare domain. Classification and prediction of medical datasets poses real challenges in Medical Data Mining. To cope with these challenges Logistic Regression (LR) and Artificial Neural Network (ANN) are commonly used. LR enables us to investigate the relationship between a categorical outcome and a set of explanatory variables. LR explains that there can be one or more independent variables that can determine the problem outcome. ANN resembles the human brain and here the information is processed by simple elements called neurons and signals are transmitted between the neurons. Feature subset selection selects subsets of features that are enough to explain the target concept. In this paper feature selection methods like forward selection and backward elimination using mean evaluation are used on the medical datasets. LR and ANN are applied on feature selection methods using Cross Validation Sample (CVS) and Percentage Split as test options. From the experimental results it is identified that for SPECTF dataset LR using percentage split prediction accuracy of 83.95% is achieved, for Diabetes Dataset LR using percentage split prediction accuracy of 80.46% is achieved, and for Liver Disorder dataset NN using percentage split prediction accuracy of 74.75% is achieved.

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Automatic Prediction System of Dengue Haemorrhagic-Fever Outbreak Risk by Using Entropy and Artificial Neural Network

Cited by 31 publications

References 3 publications

Performance of Hybrid GANN in Comparison with Other Standalone Models on Dengue Outbreak Prediction

Performance of Hybrid GANN in Comparison with Other Standalone Models on Dengue Outbreak Prediction

A Prediction System of Dengue Fever Using Monte Carlo Method

Classification and Prediction Model using Hybrid Technique for Medical Datasets

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