Machine learning for dengue outbreak prediction: A performance evaluation of different prominent classifiers

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.