Application of Machine Learning for SARS-CoV-2 Outbreak

Authors

  • Vina Ayumi  Faculty of Computer Science, Universitas Mercu Buana, Jakarta Barat, Indonesia

DOI:

https://doi.org/10.32628/IJSRSET207539

Keywords:

SARS-CoV-2, COVID-19, PRISMA, machine learning

Abstract

The plan to overcome disease outbreaks due to the novel Coronavirus (SARS-CoV-2) can be viewed from various sides, including the role of computer technology namely machine learning. This technology has been used to solve many problems, including medical-related problems. Due to the importance of research study of machine learning on COVID-19 issues, this research aim is to review literature of application of machine learning for COVID-19 outbreak by using PRISMA methodology. We obtained sixteen research articles as research data. As a result, we identified there three main aims of research study of machine learning on COVID-19 issues, including patient detection (based on the symptoms), epidemic trends or prediction, and social impact. Moreover, the method of machine learning that has been identified to solve COVID-19 issues, including Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (K-NN), Logistic Growth Forecasting Model, Naïve Bayes, Unbiased Hierarchical Bayesian Estimator, Biterm Topic Model (BTM), Support Vector Regression (SVR), Confidence-Aware Anomaly Detection (CAAD), Deep Learning Survival Cox (DLSC), Partial Derivative Regression and Nonlinear Machine Learning (PDR-NML).

References

  1. C. Sohrabi et al., “World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19),” Int. J. Surg., 2020.
  2. S. Lalmuanawma, J. Hussain, and L. Chhakchhuak, “Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review,” Chaos, Solitons & Fractals, p. 110059, 2020.
  3. I. Ranggadara, Y. S. Sari, S. Dwiasnati, and I. Prihandi, “A Review of Implementation and Obstacles in Predictive Machine Learning Model at Educational Institutions,” in Journal of Physics: Conference Series, 2020, vol. 1477, p. 32019.
  4. M. Sadikin and I. Wasito, “Translation and classification algorithm of FDA-Drugs to DOEN2011 class therapy to estimate drug-drug interaction,” in The 2nd International Conference on Information Systems for Business Competitiveness, 2013.
  5. Y. Gao et al., “Structure of the RNA-dependent RNA polymerase from COVID-19 virus,” Science (80-. )., vol. 368, no. 6492, pp. 779–782, 2020.
  6. A. Liberati et al., “The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration,” J. Clin. Epidemiol., vol. 62, no. 10, pp. e1-34, 2009.
  7. D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, “Systematic Reviews and Meta-Analyses: The PRISMA Statement,” Annu. Intern. Med., vol. 151, no. 4, pp. 264–269, 2009.
  8. R. Mohamed, M. Ghazali, and M. A. Samsudin, “A Systematic Review on Mathematical Language Learning Using PRISMA in Scopus Database,” Eurasia J. Math. Sci. Technol. Educ., vol. 16, no. 8, p. em1868, 2020.
  9. A. A. Ardakani, A. R. Kanafi, U. R. Acharya, N. Khadem, and A. Mohammadi, “Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks,” Comput. Biol. Med., p. 103795, 2020.
  10. T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. R. Acharya, “Automated detection of COVID-19 cases using deep neural networks with X-ray images,” Comput. Biol. Med., p. 103792, 2020.
  11. L. Sun et al., “Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19,” J. Clin. Virol., p. 104431, 2020.
  12. J. Wu et al., “Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results,” medRxiv, 2020.
  13. M. A. Elaziz, K. M. Hosny, A. Salah, M. M. Darwish, S. Lu, and A. T. Sahlol, “New machine learning method for image-based diagnosis of COVID-19,” PLoS One, vol. 15, no. 6, p. e0235187, 2020.
  14. P. Wang, X. Zheng, J. Li, and B. Zhu, “Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics,” Chaos, Solitons & Fractals, vol. 139, p. 110058, 2020.
  15. J. Samuel, G. G. Ali, M. Rahman, E. Esawi, and Y. Samuel, “Covid-19 public sentiment insights and machine learning for tweets classification,” Information, vol. 11, no. 6, p. 314, 2020.
  16. L. Li et al., “Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT,” Radiology, 2020.
  17. S. Vaid, C. Cakan, and M. Bhandari, “Using machine learning to estimate unobserved COVID-19 infections in North America,” J. Bone Joint Surg. Am., 2020.
  18. T. Mackey et al., “Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study,” JMIR Public Heal. Surveill., vol. 6, no. 2, p. e19509, 2020.
  19. M. Loey, G. Manogaran, M. H. N. Taha, and N. E. M. Khalifa, “A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic,” Measurement, vol. 167, p. 108288, 2020.
  20. A. Di Castelnuovo et al., “Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study,” Nutr. Metab. Cardiovasc. Dis., 2020.
  21. M. Yadav, M. Perumal, and M. Srinivas, “Analysis on novel coronavirus (COVID-19) using machine learning methods,” Chaos, Solitons & Fractals, vol. 139, p. 110050, 2020.
  22. J. Zhang, Y. Xie, Y. Li, C. Shen, and Y. Xia, “Covid-19 screening on chest x-ray images using deep learning based anomaly detection,” arXiv Prepr. arXiv2003.12338, 2020.
  23. W. Liang et al., “Early triage of critically ill COVID-19 patients using deep learning,” Nat. Commun., vol. 11, no. 1, pp. 1–7, 2020.
  24. D. P. Kavadi, R. Patan, M. Ramachandran, and A. H. Gandomi, “Partial derivative nonlinear global pandemic machine learning prediction of covid 19,” Chaos, Solitons & Fractals, vol. 139, p. 110056, 2020.
  25. Q. Li, W. Feng, and Y.-H. Quan, “Trend and forecasting of the COVID-19 outbreak in China,” J. Infect., vol. 80, no. 4, pp. 469–496, 2020.
  26. X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, “Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2097–2106.
  27. E. Chen, K. Lerman, and E. Ferrara, “Covid-19: The first public coronavirus twitter dataset,” arXiv Prepr. arXiv2003.07372, 2020.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2020-10-30

Issue

Volume 7, Issue 5, September-October-2020

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Vina Ayumi "Application of Machine Learning for SARS-CoV-2 Outbreak" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 5, pp.241-248, September-October-2020. Available at doi : https://doi.org/10.32628/IJSRSET207539