COMPARATIVE STUDY OF DECISION TREE ALGORITHM AND NAIVE BAYES CLASSIFIER FOR SWINE FLU PREDICTION

Mangesh J. Shinde, S. S. Pawar

Abstract: The modeling and analysis of the epidemic disease outbreaks in huge realistic populations is a data intensive task that requires immense computational resources. Such effective computational support becomes useful to study disease outbreak and to facilitate decision making. Epidemiology is one of the traditional approaches which are being used for studying and analyzing the outbreaks of epidemic diseases. Although useful for obtaining numbers of sick, infected and recovered individuals in a population, this traditional approach does not capture the complexity of human interactions. The model is only limited to the person to person interaction in order to track the surveillance of disease and it also having performance issues with large realistic data. In this paper we propose, the combination of computational epidemiology and modern data mining techniques with their comparative analysis for the Swine Flu prediction. The clustering algorithm K mean is used to make a group or cluster of Swine Flu suspects in a particular area. The Decision tree algorithm and Naive Bayes classifier are applied on the same inputs to find out the actual count of suspects and predict the possible surveillance of a Swine Flu in a nearby area from suspected area. The performances of these techniques are compared, based on accuracy. In our case the Naive Bayes classifier performs better than decision tree algorithm while finding the accurate count of suspects.

Keywords: Computational Epidemiology, Aerosol-borne Disease, Clustering, Predictive analysis.

DOI: https://doi.org/10.15623/ijret.2015.0406007