Dengue disease prediction using weka data mining tool

TL;DR

This paper evaluates various data mining algorithms using WEKA to predict dengue disease, identifying Naive Bayes and J48 as the most accurate classifiers with 100% accuracy on a small dataset.

Contribution

It compares multiple data mining techniques in WEKA for dengue prediction, highlighting the best algorithms for accurate classification.

Findings

Naive Bayes and J48 achieved 100% accuracy.

These algorithms had the highest ROC values and lowest error.

Naive Bayes and J48 required the least training time.

Abstract

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