PREDICTION OF HFMD DISEASE OUTBREAK FROM TWITTER

Tay, Guo Hong (2019) PREDICTION OF HFMD DISEASE OUTBREAK FROM TWITTER. [Final Year Project Report] (Unpublished)

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Abstract

Hand, foot, and mouth disease (HFMD) is a common childhood infection caused by a group of enteroviruses. This research paper describe a work about predicting of HFMD disease outbreak from Twitter. After reviewing the existing work, a proposed pipeline is being introduced. In this project, the data collection methods is extracting Twitter tweets using Twitter API with Python. The extracted tweets is going through preprocessing process. The output from this process is the corpus of HFMD disease. On the other hand, Naive Bayes and SVM algorithm is using in classification of the tweets related with HFMD disease. This is because both Naive Bayes and SVM are baseline algorithm used in text classification. In the end, a visualisation of HFMD Disease Map is presented to visualize the city that suffer HFMD outbreak using geo-located tweet that related with HFMD. Based on the Map visualisation, Malaysia is predicted to face HFMD outbreak in the period between January until March for the coming years. For the classification result, Naive Bayes and SVM provide result with accuracy of 92.8% and 96.7% respectively.

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