Abstract
Dengue disease patients are increasing rapidly and actually dengue has recorded in every continent today according to World Health Organisation (WHO) record. By WHO report the number of dengue outbreak cases announced every year has expanded from 0.4 to 1.3 million during period of 1996 to 2005 and then it has reached to 2.2 to 3.2 million during year of 2010 to 2015 respectively. Consequently , it is fundamental to have a structure that can adequately perceive the pervasiveness of dengue outbreak in a large amount of specimens momentarily. At this critical moment, the capability of seven prominent machine learning systems was assessed for forecast of dengue outbreak. These methods are evaluated by eight miscellaneous performance parameters. LogitBoost ensemble model is reported as the topmost classification accuracy of 92% with sensitivity and specificity of 90 and 94 % respectively.
Highlights
Dengue fever is the most well-known arboviral disease transmitted by female mosquitoes (Aedes Aegypti) in tropical and subtropical regions throughout the world [7]
We have shown the comparison among accuracy rate, sensitivity and specificity of the prominent classifier with two ensemble models i.e. Random forest [5] and LogitBoost
It is declared from the results that LogitBoost predicts the topmost number of true positives and it predicts the topmost number of true negatives (number of records predicted as negative and it doesn't have dengue outbreak (Table 2; Figure 4)
Summary
Dengue fever is the most well-known arboviral disease transmitted by female mosquitoes (Aedes Aegypti) in tropical and subtropical regions throughout the world [7]. Spanish word dengue is derived from dinga. Dengue fever familiar as break-bone fever, break heart fever, and dandy fever. Dengue viral fever is originated by four concerned viruses known as DEN- (1 to 4). DEN-5 which is newly introduced in 2013 [13,3]. Dengue fever (DF), Dengue Hemorrhagic Fever (DHF), and Dengue Shock Syndrome (DSS) are the broad stages of dengue viral from normal to serious respectively [8,16]
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