Open AccessDengue Fever Prediction using Datamining Classification Technique
Author(s) -
Dr.R. Anusha*
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d8810.118419
Subject(s) - naive bayes classifier , dengue fever , bayes' theorem , decision tree , artificial intelligence , computer science , dengue virus , machine learning , random forest , data mining , pattern recognition (psychology) , support vector machine , bayesian probability , medicine , virology
Dengue is a life threatening disease in all the developed countries like India. This is a virus borne disease caused by breeding of Aedesmosquito. Dengue is caused by female mosquitoes. A predictive system which can identify and minime the loss due to this problem can be constructed Datasets used is here the body temperature ,vomiting,metallic taste,joint pain etc.. the main objective ofthis paper is to classify data and to identify the maximum accuracy to predict the dengue fever using description like yes /no. So the classification techniques used here is Bayes classification ,nearest neighbor (knn),naïve bayes,rule bayes,id3,and decision tree .from the classified algorithms Naïve bayes had occurred maximum accuracy of 72%.Rapid miner is the data mining tool used to classify the data mining techniques.