Identification of Influential Parameter for Early Detection of Dengue Using Machine Learning Approach

Abstract

Dengue is a viral disease which affects public health every year in global wise. Every change in climate in a particular location increases the probability of spreading dengue disease in that domain. There are number of health schemes running by governments to prevent and control dengue disease at early stages. The usage of information technology helps to achieve this goal. There is a huge need to develop machines which enable medical technicians to detect dengue disease at early stages. For achieving this goal, the author efforts to detect dengue dataset which helpful to develop a machine learning prediction model for dengue disease. The author conducts analytical study by collecting symptoms and clinical tests conducting by researcher in the same domain. To detect important factors of dengue, the author uses the statistical and support machine. Here, author effort shows four important factors fever, headache, skin rash and abdominal pain used to detect dengue at early stage.