Ensemble-based Disease Outbreak Detection: Comparative Analysis of Health News Information Retrieval Techniques
DOI:
https://doi.org/10.47839/ijc.23.4.3547Keywords:
Ensemble learning, Epidemic surveillance, Outbreak detection, Text mining, Natural language processingAbstract
In India, Kerala is the first state to report a COVID-19 infection case, in January 2020, in a medical student, who returned from Wuhan, China. More recently, in June 2022, Kerala also reported India's first case of monkeypox disease. News websites often publish articles dedicated to reporting disease occurrences and live updates of outbreaks. Through the utilization of data gathered from online digital resources, early detection of outbreaks is possible, and this potential is already identified by the research community. As webpages give a comprehensive collection of reports covering a wide range of themes through hyperlinks, precisely categorizing news articles based on their headlines and retrieving health news is a tedious operation. Hence, this paper proposes a novel and efficient news retrieval technique grounded on an ML-based classification method with an ensemble learning approach to identify reports of disease occurrences from web pages by focusing specifically on the health context of Kerala and a comparison with baseline methods for information retrieval such as keyword-based, phrase-based, and content-based latent semantic analysis method is made.
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