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Erschienen in:
Epileptic Seizure Detection Using CNN Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

Machine Learning Based Prediction of H1N1 and Seasonal Flu Vaccination

verfasst von : Srividya Inampudi, Greshma Johnson, Jay Jhaveri, S. Niranjan, Kuldeep Chaurasia, Mayank Dixit

Erschienen in: Advanced Computing

Verlag: Springer Singapore

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Abstract

The H1N1 Flu that came into existence in 2009 had a great impact on the lives of people around the world. It was a life-threatening season to hundreds of people mainly below 65 years old which eventually made the World Health Organization (WHO) to declare it as the greatest pandemic in more than 40 years. To find out the vaccination status National 2009 H1N1 Flu Survey (NHFS) was conducted in U.S. In this paper, the data from the above survey was used to develop a model that predicts how likely people got H1N1 and seasonal flu vaccine. For this purpose, various Machine Learning (ML) and Artificial Neural Network (ANN) models are used to determine the probability of person receiving H1N1 and Seasonal Flu vaccine.

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Literatur
1.
2.
3.
4.
5.
6.
Signorini, A., Segre, A.M., Polgreen, P.M.: The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PLoS ONE 6(5), e19467 (2011)
7.
Marcel, S., Khandelwal, S.: Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control. PLoS Comput. Biol. 7(10), e1002199 (2011)CrossRef
8.
Mabrouk, M.S., Marzouk, S.Y.: A chaotic study on pandemic and classical (H1N1) using EIIP sequence indicators. In: Proceedings of 2nd International Conference on Computer Technology and Development, Cairo, pp. 218–221 (2010)
9.
Chinh, T.T.S., Stephanus, D.S., Kwoh, C., Schönbach, C., Li, X.: A possible mutation that enables H1N1 influenza a virus to escape antibody recognition. In: Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Hong Kong, pp. 81–84 (2010)
10.
Huang, J., Lin, T., Chang, W., Hsieh, W: Aptamer-modified CNTFET biosensor for detecting H1N1 virus in droplet. In: Proceedings of 4th IEEE International NanoElectronics Conference, Tao-Yuan, pp. 1–2 (2011)
11.
Ünlü, M.S.: Optical interference for multiplexed, label-free, and dynamic biosensing: Protein, DNA and single virus detection. In: Proceedings of XXXth URSI General Assembly and Scientific Symposium, Istanbul, pp. 1–2 (2011)
12.
Kamikawa, T., et al.: Pandemic influenza detection by electrically active magnetic nanoparticles and surface plasmon resonance, nanotechnology. IEEE Trans. Nanotechnol. 11, 88–96 (2012)CrossRef
13.
Baby Jerald, A., Gopalakrishnan Nair, T.R.: Influenza virus vaccine efficacy based on conserved sequence alignment. In: Proceedings of International Conference on Biomedical Engineering (ICoBE), Penang, pp. 327–329 (2012)
14.
Chrysostomou, C., Seker, H.: Signal-processing-based bioinformatics approach for the identification of influenza A virus subtypes in Neuraminidase genes. In: Proceedings of 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3066–3069 (2013)
15.
Wiriyachaiporn, N., Sirikett, H., Dharakul, T.: Rapid influenza a antigen detection using carbon nanostrings as label for lateral flow immunochromatographic assay. In: Proceedings of 13th IEEE International Conference on Nanotechnology (IEEE-NANO 2013), Beijing, pp. 166–169 (2013)
16.
Ma, Y., Chang, W., Wang, C., Ma, H., Huang P., Lee, G.: An integrated passive microfluidic device for rapid detection of influenza a (H1N1) virus by reverse transcription loop-mediated isothermal amplification (RT-LAMP). In: Proceedings of 19th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS), Kaohsiung, pp. 722–725 (2017)
17.
18.
Stalder, F., Hirsh, J.: Open source intelligence. First Monday 7(6), 416 (2002)CrossRef
19.
Motoyama, M., Meeder, B., Levchenko, K., Voelker, G.M., Savage, S.: Measuring online service availability using Twitter. In: Proceedings of Workshop on online social networks, Boston, Massachusetts, USA (2010)
20.
Wong, L.P., Sam, I.C.: Behavioral responses to the influenza A (H1N1) outbreak in Malaysia. J. Commun. Health 34, 23–31 (2011)
21.
Bao, Y., et al.: The influenza virus resource at the national center for biotechnology information. J. Virol. 82(2), 596–601 (2008)CrossRef
22.
Hu, W.: Molecular features of highly pathogenic Avian and Human H5N1 Influenza a viruses in Asia. Comput. Mol. Biosci. 2(2), 45–59 (2012)CrossRef
23.
Smith, P.J., Wood, D., Darden, P.M.: Highlights of historical events leading to national surveillance of vaccination coverage in the United States. Public Health Rep. 126(Suppl 2), 3–12 (2011)CrossRef
24.
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2012)
25.
Buitinck, L., et al.: API design for machine learning software: experiences from the scikit-learn project (2013)
26.
Dubosson, F., Bromuri, S., Schumacher, M.: A python framework for exhaustive machine learning algorithms and features evaluations. In: Proceedings of IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), Crans-Montana, pp. 987–993 (2016)
27.
Virtanen, P., Gommers, R., Oliphant, T.E., et al.: SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17, 261–272 (2020)
Metadaten
Titel
Machine Learning Based Prediction of H1N1 and Seasonal Flu Vaccination
verfasst von
Srividya Inampudi
Greshma Johnson
Jay Jhaveri
S. Niranjan
Kuldeep Chaurasia
Mayank Dixit
Copyright-Jahr
2021
Verlag
Springer Singapore
Buch
Advanced Computing

Print ISBN: 978-981-16-0400-3

Electronic ISBN: 978-981-16-0401-0

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-981-16-0401-0_11

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