  Dengue is a life threatening disease prevalent in several developed as well
as developing countries like India.In this paper we discuss various algorithm
approaches of data mining that have been utilized for dengue disease
prediction. Data mining is a well known technique used by health organizations
for classification of diseases such as dengue, diabetes and cancer in
bioinformatics research. In the proposed approach we have used WEKA with 10
cross validation to evaluate data and compare results. Weka has an extensive
collection of different machine learning and data mining algorithms. In this
paper we have firstly classified the dengue data set and then compared the
different data mining techniques in weka through Explorer, knowledge flow and
Experimenter interfaces. Furthermore in order to validate our approach we have
used a dengue dataset with 108 instances but weka used 99 rows and 18
attributes to determine the prediction of disease and their accuracy using
classifications of different algorithms to find out the best performance. The
main objective of this paper is to classify data and assist the users in
extracting useful information from data and easily identify a suitable
algorithm for accurate predictive model from it. From the findings of this
paper it can be concluded that Na\"ive Bayes and J48 are the best performance
algorithms for classified accuracy because they achieved maximum accuracy= 100%
with 99 correctly classified instances, maximum ROC = 1, had least mean
absolute error and it took minimum time for building this model through
Explorer and Knowledge flow results
