Open AccessCOVID-19 epidemic: analysis and prediction
Author(s) -
Santosini Bhutia,
Bichitrananda Patra,
Mitrabinda Ray
Publication year - 2022
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v11.i2.pp736-745
Subject(s) - covid-19 , computer science , support vector machine , regression analysis , regression , process (computing) , representation (politics) , linear regression , artificial intelligence , machine learning , data mining , operations research , statistics , disease , medicine , infectious disease (medical specialty) , mathematics , pathology , politics , political science , law , operating system
“Novel Coronavirus”, commonly known as COVID-19 has spread nearly to the entire world. The number of impacted cases and deaths has increased significantly in each country, posing a challenge for the world’s health organizations. The goal of this paper was to better comprehend and analyze the growth of the disease in India, including confirmed, recovered, fatalities, and active cases of COVID-19. Data analysis affects an organization’s decision-making process with interactive visual representation. The proposed model was an ensemble model that was built using linear regression, polynomial regression, and support vector machine (SVM) regression models. The model predicted the number of confirmed cases from 30 th May 2021 to 15 th June 2021 based on the data available from 22 January 2020 to 29 May 2021 and improved accuracy was obtained when compared with the actual data. Forecasting the confirmed cases might assist health organizations in planning medical facilities. Following that, an appropriate machine leraning (ML) model must be found that can predict the number of new cases in the future.