Dengue Fever Prediction: A Data Mining ProblemKamran Shaukat1*, Nayyer Masood2, Sundas Mehreen1 and Ulya Azmeen1
- *Corresponding Author:
- Kamran Shaukat
IT Department, University of the Punjab
Jhelum Campus, Pakistan
E-mail: [email protected]
Received Date: June 04, 2015 Accepted Date: October 19, 2015 Published Date: October 25,2015
Citation: Shaukat K, Masood N, Mehreen S, Azmeen U (2015) Dengue Fever Prediction: A Data Mining Problem. J Data Mining Genomics Proteomics 6:181. doi: 10.4172/2153-0602.1000181
Copyright: © 2015 Shaukat K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Dengue is a threatening disease caused by female mosquitos. It is typically found in widespread hot regions. From long periods of time, Experts are trying to find out some of features on Dengue disease so that they can rightly categorize patients because different patients require different types of treatment. Pakistan has been target of Dengue disease from last few years. Dengue fever is used in classification techniques to evaluate and compare their performance. The dataset was collected from District Headquarter Hospital (DHQ) Jhelum. For properly categorizing our dataset, different classification techniques are used. These techniques are Naïve Bayesian, REP Tree, Random tree, J48 and SMO. WEKA was used as Data mining tool for classification of data. Firstly we will evaluate the performance of all the techniques separately with the help of tables and graphs depending upon dataset and secondly we will compare the performance of all the techniques.