Dengue Fever Prediction using Datamining Classification Technique
Dr. R.Anusha, Asst. Professor, Department of Computer Science, M.O.P.Vaishnav College For Women(Autonomous),Chennai.

Manuscript received on November 12, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on 30 November, 2019. | PP: 8685-8688 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8810118419/2019©BEIESP | DOI: 10.35940/ijrte.D8810.118419

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Abstract: Dengue is a life threatening disease in all the developed countries like India. This is a virus borne disease caused by breeding of Aedes mosquito. 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 of this 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.
Keywords: Decision tree, Naïve Bayes, Rapidminer.
Scope of the Article: Regression and Prediction.