Predictive Analytics of Dengue Disease using Machine Learning Classification Approaches

Abstract

The main objective of proposed system is to develop a system which can use the existing related Information to answer the causes of dengue and make use of this information to forecast the dengue occurrence within a  specific region so that the medical experts can predict, control and manage the epidemic of disease at the earliest. Dengue disease is one of the major disease which causes to deaths for the many people with lack of experience. According to the surveys there are 390 million dengue cases reported per year. By collecting the data from various repositories and with the combination of predictor variables such as meteorological data, disease surveillance, health data and socioeconomic data one can able to predict the precision of the model. This disease majorly effect on the Asian and American regions. Dengue disease is caused by the Aedes aegypti and Aedes albopictus mosquito bite,  it spread other viruses such as yellow fever, chickengunya. Now Dengue has become the global problem for most of the countries. The best way to treat the disease is to predict the symptoms early and take necessary precautions to prevent the disease. In this paper, we present various machine learning algorithms to detect the dengue at the early stages which will be helpful to avoid from death. This paper focuses on better understanding of geospatial insights, health care monitor and management systems.