ABSTRACT in muscles and a rash [2].The


The main objective of this research
is to use o the classification techniques to predict the number of Dengue fever
prone cases in Jhelum district and in surrounding  near by areas. We have compared performance
rate of different classification techniques and algorithms through this
research paper. The general
agenda of this paper is to classify dataset so that users can fetch useful and
ample of information and easily identify a suitable algorithm and technique  for accurate and precise predictive model from
this paper . Naive Bayes, J48 and SMO are the best  suitable  algorithms for classified accuracy as they
achieved maximum accuracy= 100% with 98 correctly classified instances, maximum
ROC = 1 with least mean absolute error.


Dengue infection is a
major disease caused by dengue germ, which infect in body of human by female
mosquito 1. Various  Symptoms include headache, sudden-onset fever, retro orbital pain, joint-pain, pain in muscles
and a rash 2.The other
name for dengue is, “breakbone fever”, that comes from the associated
muscle and joint pains. Dengue
infection is a widespread disease and has endangered 2.5 billion populations
all around the universe. Every year about 50 million of people suffer from this
life-taking disease globally 1.

According to world
health organization researches, dengue infection is divided into two major types,
i.e., type 1 and type 2, 3. First one is classical and traditional one dengue
called dengue fever and the other is called as dengue hemorrhagic fever. DHF1,
DHF2, DHF3 and DHF4 are further four 
categories of dengue hemorrhagic fever. DHF is initiated by start of
fever which continues for 3 to 7 days with 
signs including like leakage of plasma, shock and weak pulse.

Different techniques and algorithms  for dengue fever classification can be degined
and used such as Naïve Bayes classifier; decision tree, KNN Technique,
multilayered Technique and SVM 1,4,5. These techniques are evaluated based on
five common measures in data minning : accuracy, precision, sensitivity,
specificity and negative rate.

Some researchers have been working on
dengue  classification such as Mr. Tanner
et al. and Tarig et al. Tanner’s team used one of the best algorithms of data
mining the Decision tree approach and they classified upto 1200 patients record
 and found 6 remarkable  and important features and aspects3. They
got 84% accuracy rate . Tarig’s team used techni que of  Self Organizing MAP
(SOM) and ML feed-forward neural networks (MFNN). They grouped patients into
two sets and got only 70% correctness measure whereas Fatimah Ibrahim et.al
used ML perceptron’s (MLP) and got upto 90% accuracy. Daranee et al.
elaboarated using decision tree method to group dengue patients from two data
sets4. They got 97.6% and 96.6% accuracy  rate from first and second method respectively.
We use the following  algorithms and techniques: Naïve Bayesian,
J48, SMO, REP Tree and Random tree5. WEKA tool was used as Data mining tool
for classification of data.