ABSTRACT:
The knowledge discovery in databases (KDD) is alarmed by the development of methods and techniques for the use of data. Data mining is one of the most critical phases of the KDD. Data mining is a method of pattern discovery and extraction where there is a large amount of data involved. Electronic health records are becoming increasingly common in health care organizations. With increased access to a substantial amount of patient data, healthcare companies are now in a position to optimize the efficiency and quality of their businesses through data mining. COVID-19 is a new global epidemic in 186 countries around the world. And as a result of this pandemic, patient data is being introduced at a quicker rate. Search engines have valuable data from populations and this data can be useful for the study of epidemics. Using data mining tools for available data will provide deeper insight into the management of the coronavirus outbreak health problem for each country and the world. In order to contribute to the well-being of the living population, the research will analyze coronavirus actions in the previous months and will display statistics using different models, data mining techniques. Various data mining models and methods will demonstrate the pattern of the COVID-19 over the year.
Cite this article:
Aman Jatain. Data Mining for Predicting the Covid-19 Pattern. Research Journal of Engineering and Technology. 2021;12(3):79-4. doi: 10.52711/2321-581X.2021.00013
Cite(Electronic):
Aman Jatain. Data Mining for Predicting the Covid-19 Pattern. Research Journal of Engineering and Technology. 2021;12(3):79-4. doi: 10.52711/2321-581X.2021.00013 Available on: https://ijersonline.org/AbstractView.aspx?PID=2021-12-3-4
REFERENCES:
1. H. C. Koh and G. Tan. Data Mining Application in Healthcare. Journal of Healthcare Information Management. 2005, 19(2): 64-72.
2. D. Hand, H. Mannila and P. Smyth. Principles of data mining. MIT Press. 2001: 546.
3. Stoecklin, Sibylle Bernard, Patrick Rolland, Yassoungo Silue, Alexandra Mailles, Christine Campese, Anne Simondon, Matthieu Mechain. First cases of coronavirus disease 2019 (COVID-19) in France: Surveillance, Investigations and Control Measures. Euro-surveillance. 2020, 25(6): 2000094.
4. WHO coronaviruses (COVID-19). Retrieved March 30, 2020 from https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
5. Singh, R., Singh, R., Bhatia, A. Sentiment analysis using Machine Learning technique to predict outbreaks and epidemics. International Journal of Advance Science and Research. 2018, 3(2):19-24.
6. Lauer, Stephen A., Kyra H. Grantz, Qifang Bi, Forrest K. Jones, Qulu Zheng, Hannah R. Meredith, Andrew S. Azman, Nicholas G. Reich, and Justin Lessler. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Annals of internal medicine. 2020, 172(9):577-582.
7. Singer, H. M. Short-term predictions of country-specific Covid-19 infection rates based on power law scaling exponents. arXiv preprint arXiv:2003. 2020: 1-6.
8. Milley, A. Healthcare and data mining. Health Management Technology. 200, 21(8):44-47.
9. Trybula, W.J. Data mining and knowledge discovery. Annual Review of Information Science and Technology. 1997, 32: 197-229.
10. Chakraborty, T., and Ghosh, I. Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis. Chaos, Solitons & Fractals. 2020, 135: 1-7. 109850.