Portal de Periódicos da CAPES

Classification Of Dengue Fever Using Decision Tree

2014; Volume: 3; Issue: 2 Linguagem: Inglês

10.21015/vtcs.v3i2.108

2411-6335

Wajeeha Farooqi, Sadaf Ali, Abdul Wahab,

Imbalanced Data Classification Techniques

Dengue fever is widespread disease in the tropical areas caused by bite of female Eddie mosquito. Pakistan has been victim of this rapidly growing disease since last few years. The world health organization identified the four types of dengue fever. The experts are facing the problem of misdiagnosis of dengue fever. The Tests needed for empirical Classification of Dengue Fever takes a lot of time and money especiallyin epidemic situation in a country with limited resources. Therefore, we have used data mining techniques for the efficient classification of the dengue fever Type. The decision tree learning algorithm has been used as a classification Model. We performed two experiments using decision tree. The first general experiment shows the accuracy of 99.44%. It prunes the attributes which classify the dengue fever on the basis of the values in the dataset. The Second experiment classifies the dengue fever on the basis of expert weighted attributes, which are used in the classification on the basis of Minimum Cost and resource availability. The accuracy of this Model is still high 98.62%. We compared the performance in term of Type II error. We found that the Type II error is very low in second experiment