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Analysis on novel coronavirus (COVID-19) using machine learning methods
- Source :
- Chaos, Solitons & Fractals, Chaos, Solitons, and Fractals
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- In this paper, we are working on a pandemic of novel coronavirus (COVID-19). COVID-19 is an infectious disease, it creates severe damage in the lungs. COVID-19 causes illness in humans and has killed many people in the entire world. However, this virus is reported as a pandemic by the World Health Organization (WHO) and all countries are trying to control and lockdown all places. The main objective of this work is to solve the five different tasks such as I) Predicting the spread of coronavirus across regions. II) Analyzing the growth rates and the types of mitigation across countries. III) Predicting how the epidemic will end. IV) Analyzing the transmission rate of the virus. V) Correlating the coronavirus and weather conditions. The advantage of doing these tasks to minimize the virus spread by various mitigation, how well the mitigations are working, how many cases have been prevented by this mitigations, an idea about the number of patients that will recover from the infection with old medication, understand how much time will it take to for this pandemic to end, we will be able to understand and analyze how fast or slow the virus is spreading among regions and the infected patient to reduce the spread based clear understanding of the correlation between the spread and weather conditions. In this paper, we propose a novel Support Vector Regression method to analysis five different tasks related to novel coronavirus. In this work, instead of simple regression line we use the supported vectors also to get better classification accuracy. Our approach is evaluated and compared with other well-known regression models on standard available datasets. The promising results demonstrate its superiority in both efficiency and accuracy.
- Subjects :
- Active cases
Coronavirus disease 2019 (COVID-19)
Computer science
General Mathematics
Simple Linear Regression
General Physics and Astronomy
Pearson
medicine.disease_cause
Machine learning
computer.software_genre
01 natural sciences
Article
010305 fluids & plasmas
Novel Coronavirus
Recoveries
0103 physical sciences
Pandemic
medicine
010301 acoustics
Coronavirus
Polynomial regression
business.industry
Applied Mathematics
COVID-19
Regression analysis
Statistical and Nonlinear Physics
Polynomial Regression
Support vector machine
Infectious disease (medical specialty)
Support Vector Regression model
Artificial intelligence
Simple linear regression
business
computer
Subjects
Details
- Language :
- English
- ISSN :
- 09600779
- Database :
- OpenAIRE
- Journal :
- Chaos, Solitons & Fractals
- Accession number :
- edsair.doi.dedup.....7168b6125c3e20b4a7c7409400bfcee6
- Full Text :
- https://doi.org/10.1016/j.chaos.2020.110050