IEEE Style Citation: M. Vennela, G. Lavanya Devi, P.R.S. Naidu, “Analysing COVID- 19 Cases by Eliminating False Negatives and False Positives through Machine Learning Approach,” International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.71-74, 2020.
MLA Style Citation: M. Vennela, G. Lavanya Devi, P.R.S. Naidu "Analysing COVID- 19 Cases by Eliminating False Negatives and False Positives through Machine Learning Approach." International Journal of Computer Sciences and Engineering 8.10 (2020): 71-74.
APA Style Citation: M. Vennela, G. Lavanya Devi, P.R.S. Naidu, (2020). Analysing COVID- 19 Cases by Eliminating False Negatives and False Positives through Machine Learning Approach. International Journal of Computer Sciences and Engineering, 8(10), 71-74.
BibTex Style Citation:
@article{Vennela_2020,
author = {M. Vennela, G. Lavanya Devi, P.R.S. Naidu},
title = {Analysing COVID- 19 Cases by Eliminating False Negatives and False Positives through Machine Learning Approach},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {10 2020},
volume = {8},
Issue = {10},
month = {10},
year = {2020},
issn = {2347-2693},
pages = {71-74},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5233},
doi = {https://doi.org/10.26438/ijcse/v8i10.7174}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v8i10.7174}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5233
TI - Analysing COVID- 19 Cases by Eliminating False Negatives and False Positives through Machine Learning Approach
T2 - International Journal of Computer Sciences and Engineering
AU - M. Vennela, G. Lavanya Devi, P.R.S. Naidu
PY - 2020
DA - 2020/10/31
PB - IJCSE, Indore, INDIA
SP - 71-74
IS - 10
VL - 8
SN - 2347-2693
ER -
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Abstract
Novel coronavirus (COVID-19 or 2019-nCoV) pandemic which doesn’t have neither a clinically proven vaccine nor drugs. As the No. of Cases Increasing Day by Day, the public was panicking. In the process of increasing Number of Tests, Some Rapid tests are also Taking place. If you take these rapid tests into consideration, where we are getting false results like False Positives, True Positives which results in a panic among the people who have tested. Due to these false results, the public was in a panic situation. To avoid that panic among the public, we define machine learning approach to predict the COVID where it represents False Positive rate, and True Positive rate through ROC(Receiver Operating Characteristic) curve and, we also get a Confusion Matrix which visually represents True Negatives, False Positives, False Negatives and True positives and it also generates a new dataset from a given dataset, by eliminating them without sampling using clinical spectrum data of ‘SARS-Cov-2 exam result’.
[1] Jay Furst-“False-negative COVID-19 test results may lead to a false sense of security”. Source: mayo clinic
[2] Steven Woloshin, M.D., Neeraj Patel, B.A., and Aaron S. Kesselheim, M.D., J.D., M.P.H.-“False Negative Tests for SARS-CoV-2 Infection — Challenges and Implications, Journal published on June 5, 2020, in The New England Journal of medicine”.
[3] John Wiley& sons “Machine Learning: Hands-on for Developers and Technical Professionals”
[4] Lin Jia Kewen Li?Yu Jiang Xin Guo Ting zhao, "Prediction and analysis of Coronavirus Disease", Populations and Evolution, 2020.
[5] Sanjib Halder “A Mathematical Model to Forecast & Compare Covid-19 Outbreak in Male & Female using Polynomial Regression Analysis”-IJCSE ,vol.8,issued on 5,May 2020.
[6] Saroj S. Date “Forecasting novel Covid-19 confirmed cases in India using Machine Learning Methods” –IJCSE, vol.8, issued on 6,June 2020.
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