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Trend and Predictive Analytics of Dengue Prevalence in Administrative Region
Premalatha K, , Kannimuthu Subramanian, Swathypriyadharsini P
Published in IGI Global
Pages: 236 - 262
Dengue is fast emerging pandemic-prone viral disease in many parts of the world. Dengue flourishes in urban areas, suburbs, and the countryside, but also affects more affluent neighborhoods in tropical and subtropical countries. Dengue is a mosquito-borne viral infection causing a severe flu-like illness and sometimes causing a potentially deadly complication called severe dengue. It is a major public health problem in India. Accurate and timely forecasts of dengue incidence in India are still lacking. In this chapter, the state-of-the-art machine learning algorithms are used to develop an accurate predictive model of dengue. Several machine learning algorithms are used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed, and it is found that the optimized SVR gives minimal RMSE 0.25. The classifiers are applied, and experiment results show that the extreme boost and random forest gives 93.65% accuracy.
About the journal
JournalHandbook of Research on Applications and Implementations of Machine Learning Techniques Advances in Computational Intelligence and Robotics
PublisherIGI Global
Open Access0