چکیده (انگلیسی):

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).