Dengue Fever Prediction Using Data Mining Technique

Abstract

Data mining has ability to extract useful knowledge that is hidden in huge data. Health care system is potential area to apply and take the advantage of data mining. Dengue fever is a disease caused by a family of viruses transmitted by mosquitoes. To detect the dengue fever at the beginning by using the most important medical symptoms and laboratory data helps to predict the dengue fever in early stages. This process consists of three important steps: to find manual missing value imputation method is applied that makes the data consistent; to select most significant attributes for dengue fever; under the classification technique, dengue fever is used to calculate and to associate their performance. From the UCI repository all the dataset were collected and for that different classification techniques are performed. There are Decision tree, and Support Vector Machine. WEKA is used as a tool in data mining for classification of data. In the conclusion we propose a new technique to predict the dengue fever in the early stages by applying data mining technique on the medical data bases.