Asian Journal of Computer Science and Technology (AJCST)
Influenza Prediction: Analyzing Machine Learning AlgorithmsAuthor : Sapna Yadav and Pankaj Agarwal
Volume 9 No.1 January-June 2020 pp 14-18
Analyzing online or digital data for detecting epidemics is one of the hot areas of research and now becomes more relevant during the present outbreak of Covid-19. There are several different types of the influenza virus and moreover they keep evolving constantly in the same manner the COVID-19 virus has done. As a result, they pose a greater challenge when it comes to analyzing them, predicting when, where and at what degree of severity it will outbreak during the flu season across the world. There is need for greater surveillance to both seasonal and pandemic influenza to ensure the health and safety of the mankind. The objective of work is to apply machine learning algorithms for building predictive models that can predict where the occurrence, peak and severity of influenza in each season. For this work we have considered a freely available dataset of Ireland which is recorded for the duration of 2005 to 2016. Specifically, we have tested three ML Algorithms namely Linear Regression, Support Vector Regression and Random Forests. We found Random Forests is giving better predictive results. We also conducted experiment through weka tool and tested Zero R, Linear Regression, Lazy Kstar, Random Forest, REP Tree, Multilayer Perceptron models. We again found the Random Forest is performing better in comparison to all other models. We also evaluated other regression models including Ridge Regression, modified Ridge regression, Lasso Regression, K Neighbor Regression and evaluated the mean absolute errors. We found that modified Ridge regression is producing minimum error. The proposed work is inclined towards finding the suitability & appropriate ML algorithm for solving this problem on Flu.
Epidemics, Influenza Virus, Linear Regression, Support Vector Regression and Random Forests, Zeror, Linear Regression, Lazy Kstar, Random Forest, Reptree
- “Influenza (Seasonal)”. World Health Organization (WHO). 6 November 2018. Archivedfrom the original on 30 November 2019. Retrieved 30 November 2019.
- “Key Facts about Influenza (Flu)”. Centers for Disease Control and Prevention (CDC). 9 September 2014. Archivedfrom the original on 2 December 2014. Retrieved 26 November 2014.
- Li Zhang, Haixin Ai, Qi Zhao, Junfeng Zhu, Wen Chen, Xuewei Wu, Liangchao Huang, Zimo Yin, Jian Zhao, and Hongsheng Liu, “Computational Prediction of Inﬂuenza Neuraminidase Inhibitors Using Machine Learning Algorithms and Recursive Feature Elimination Method,” Virology, Vol.352, pp. 418-426, 2006.
- Ginsberg, Mohebbi, Patel, Brammer, Smolinski & Brilliant, “Detecting influenza epidemics using search engine query data” published by Nature, 2009.
- Aramaki Eiji, Sachiko Maskawa, Mizuki Morita. Twitter catches the flu: detecting influenza epidemics using Twitter. Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, 2011.
- Chen, Po-Fon, et al. “Theoretical assessment of immunogenicity variation on the HA protein of H2N2 influenza virus by using fuzzy integral and SVM classifier.” International Conference on Machine Learning and Cybernetics. Vol. 2. IEEE, 2011.
- Broniatowski David A, Michael J Paul, Mark Dredze. “National and local influenza surveillance through twitter: An analysis of the 2012-2013 influenza epidemics”, PloS one.Vol.8, No.12,2013; e83672
- Liu L, Han M, Zhou Y, Wang Y., “LSTM recurrent neural networks for influenza trends prediction”, In: International symposium on bioinformatics research and applications. Springer, Cham, pp. 259–264, 2018.
- Zhang J, Nawata K, Multi-step prediction for influenza outbreak by an adjusted long-short term memory. Epidemiol Infect146, No.7, pp.809–816, 2018.
- Yang CT, Chen CJ, Tsan YT, Liu PY, Chan YW, Chan WC, “An implementation of real time air quality and influenza-like illness data storage and processing platform”, Comput Hum Behav,2018
- Xue, H., Bai, Y., Hu, H., & Liang, H., “Regional level influenza study based on Twitter and machine learning method.” PloS one4, 2019.
- Yang, Chao-Tung, et al. “Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources.” The Journal of Supercomputing, 2020.