Prediction of dengue using data mining classification algorithms
Abstract
Dengue is a life-threatening disease prevalent in several developed as well as developing countries like India. This is a virus born disease caused by breeding of Aedes mosquito. Datasets that are available for dengue describe information about the patients suffering with dengue disease. Dengue disease has symptoms like: Fever Temperature, WBC, Platelets, Severe Headache, Vomiting, Metallic Taste, Joint Pain, Appetite, Diarrhea, Hematocrit, Hemoglobin, and how many days suffer in different city. The main objective of this paper is to classify dengue data and assist the users in extracting useful information from data and easily identify a suitable algorithm for accurate predictive model from it. The proposed system is to determine the prediction of dengue disease and their accuracy using classifications of different algorithms to find out the best performance. Data mining is a well-known technique used by health organizations for classification of diseases such as dengue, diabetes and cancer in bioinformatics research. IBM Watson Analytics is used to analyze the influence of different parameters on the given data set. In the proposed approach, R programming is to evaluate data and compare results.
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