In India, people have started using twitter and nowadays, its craze has overshadowed the users all day. In India, a Twitter user across India was predicted to be more than 34 million in 2019. Twitter data is a very huge amount of data that can be used for the prediction of various diseases. Tweets are strongly related to Dengue cases. Dengue is a viral-borne disease that is also one of the widespread waterborne diseases. Nowadays people are trying a lot to avoid being a victim of dengue. But this communicable disease has highly increased alongside the urbanization rate in the tropical rain forest region. In this research paper, we focused on the retrieval of tweets using a hashtag keyword using a free analytic tool Vicinitas. We collected a set of 102 tweets to train a classifier to identify dengue, record and predict the emergence and transmission of dengue in a population. WEKA is a collection-set for machine learning and it is free open-source software. In this research, we used the dengue datasets with a total of one hundred two instances of dengue and two attributes i.e., text and class to determine accuracy using the various classifying algorithms. For the best outcome, we used seven classification techniques for accuracy. The main methodology and the techniques we used for predicting the dengue are J48, Naïve Bayesian, SMO, and Random tree, ZeroR, Random Forest and REP Tree. We after evaluating various attributes of the result finally concluded that Bayes obtained the highest accuracy rate.

Dengue, Weka, and Classification

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