IEEE Style Citation: G. Dinesh Chandra, G. Lavanya Devi, P.R.S. Naidu, “Analysing Covid-19 Cases by Eliminating False Negatives, False Positives by Visual Exploratory Data Analysis Approach,” International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.61-66, 2020.
MLA Style Citation: G. Dinesh Chandra, G. Lavanya Devi, P.R.S. Naidu "Analysing Covid-19 Cases by Eliminating False Negatives, False Positives by Visual Exploratory Data Analysis Approach." International Journal of Computer Sciences and Engineering 8.11 (2020): 61-66.
APA Style Citation: G. Dinesh Chandra, G. Lavanya Devi, P.R.S. Naidu, (2020). Analysing Covid-19 Cases by Eliminating False Negatives, False Positives by Visual Exploratory Data Analysis Approach. International Journal of Computer Sciences and Engineering, 8(11), 61-66.
BibTex Style Citation:
@article{Chandra_2020,
author = {G. Dinesh Chandra, G. Lavanya Devi, P.R.S. Naidu},
title = {Analysing Covid-19 Cases by Eliminating False Negatives, False Positives by Visual Exploratory Data Analysis Approach},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {11 2020},
volume = {8},
Issue = {11},
month = {11},
year = {2020},
issn = {2347-2693},
pages = {61-66},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5264},
doi = {https://doi.org/10.26438/ijcse/v8i11.6166}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v8i11.6166}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5264
TI - Analysing Covid-19 Cases by Eliminating False Negatives, False Positives by Visual Exploratory Data Analysis Approach
T2 - International Journal of Computer Sciences and Engineering
AU - G. Dinesh Chandra, G. Lavanya Devi, P.R.S. Naidu
PY - 2020
DA - 2020/11/30
PB - IJCSE, Indore, INDIA
SP - 61-66
IS - 11
VL - 8
SN - 2347-2693
ER -
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Abstract
COVID-19 outbreak has put the whole world in an unusual situation bringing life around the world to a frightening halt and claiming thousands of lives. To quickly trace the disease for breaking chain governments increased the no. of testings, by increasing the no. of tests there is also a raise in no. of cases but there were only limited facilities. Today Machine Learning plays a crucial role in various sectors like Business, Industries and Health care System and so on for predicting their economy. In the Healthcare system Machine Learning is used to predict the probability of occurring diseases. In this paper by Machine Learning approach we render to Predict whether a patient is corona positive or negative based on the results of laboratory tests collected from clinical dataset SARS-Cov-2 among suspected cases and we also predict whether a patient needs to be admitted into a general ward or a semi-intensive unit or an intensive care unit based on his symptoms by using some Machine Learning models like Principal Component Analysis, and visualize the data by Exploratory Data Analysis(EDA).
Key-Words / Index Term
PCA (Principal Component Analysis) ,EDA (Exploratory Data Analysis)
References
[1]. Predicting COVID Cases by Eliminating TPS, FNS, TNS, FPS through Machine Learning Approach by M. Vennela IJCSE vol8 issued on 10 oct 2020
[2]. Saroj S. Date “Forecasting novel Covid-19 confirmed cases in India using Machine Learning Methods” –IJCSE, vol.8, issued on 6,June 2020.
[3]. Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart HealthCare Saad Awadh Alanazi M. M.Kamruzzaman,MadallahAlruwaili,2NasserAlshammari,1Salman Ali Alqahtani,3 and Ali Karime4:Article in hindawi.
[4]. 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.
[5]. Jay Furst-“False-negative COVID-19 test results may lead to a false sense of security”. Source: mayo clinic Steven Woloshin, M.D., Neeraj Patel, B.A., and Aaron.
[6]. John Wiley& sons “Machine Learning: Hands-on for Developers and Technical Professionals”
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