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29 November 2023 Comparative study of machine learning methods for influenza outbreak forecasting
Yingke He
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Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129370Q (2023) https://doi.org/10.1117/12.3013588
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore
Abstract
The contagious disease influenza that is prevalent before and during the Covid-19 era poses a substantial effect on the global health care system. Traditional computational epidemiology can simulate potential disease progress but is inefficient in gathering sophisticated and real-time data sets. Social media, the world’s most widespread data generator, provides an access to more updated epidemic surveillance. Confronted with the outburst of the Covid-19 pandemic and concurrent influenza, machine learning models are the more efficient and crucial technique in mapping the next influenza outbreak with social media data. This study utilized supervised machine learning techniques including Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF) to estimate the risk of influenza outbreaks, and examined the potential of different forecasting models in predicting the outbreak. The content of the social media was modeled based on its correlations with disease outbreaks and computed through various statistical models. Comparisons of the result revealed RF as the most efficient forecasting model for influenza outbreaks during the Covid-19 pandemic and demonstrate the usefulness of this study in future disease prediction.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yingke He "Comparative study of machine learning methods for influenza outbreak forecasting", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129370Q (29 November 2023); https://doi.org/10.1117/12.3013588
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KEYWORDS
Machine learning
Data modeling
Statistical modeling
Statistical analysis
Decision trees
Random forests
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