Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/76419
Título: Prediction of COVID-19 pandemic measuring criteria using support vector machine, prophet and linear regression models in Indian scenario
Autores/as: Gupta, Amit Kumar
Singh, Vijander
Mathur, Priya
Travieso-González, Carlos M. 
Clasificación UNESCO: 240401 Bioestadística
Palabras clave: 68T01
Covid-19
Forecasting
Linear Regression Model
Machine Learning, et al.
Fecha de publicación: 2021
Publicación seriada: Journal of Interdisciplinary Mathematics 
Resumen: The whole world is embroiling the pandemic situation caused by COVID-19, which is spreading across all countries. As of mid-May, COVID-19 continues to increase the number of people affected and the number of deaths in each country. Each country’s administrations concerned are making endless efforts to maintain public health, mental health and to regulate the rate of illness of COVID-19. Analysis of COVID-19 data using the machine learning paradigm is becoming a major interest of the researcher in these situations. Several researchers analyzed data from COVID-19 to predict infection, death, cured persons in the future, which may lead to the planning of each country’s regulatory authority to maintain the public health of its people. The machine learning algorithm provides more accurate results when the data size is large due to the lower number of data sets available to COVID-19, making the most accurate predictions a challenging task to implement the machine learning algorithm. This paper was essentially designed to predict the active rate, the death rate, and the cured rate in India by analyzing the data of COVID-19. There are three models of machine learning Support Vector Machine (SVM), Prophet Forecasting Model, and Linear Regression Model for predicting active rate, death rate and cured rate. Prophet Forecasting Model has been shown to be the best predictive method for predicting active rate, death rate and cured rate compared to SVM and Linear Regression when the vast uncertain and small data sets.
URI: http://hdl.handle.net/10553/76419
ISSN: 0972-0502
DOI: 10.1080/09720502.2020.1833458
Fuente: Journal of Interdisciplinary Mathematics [ISSN 0972-0502], v. 24(1), p. 89-108
Colección:Artículos
miniatura
Adobe PDF (1,58 MB)
Vista completa

Citas SCOPUSTM   

30
actualizado el 24-mar-2024

Visitas

157
actualizado el 15-abr-2023

Descargas

459
actualizado el 15-abr-2023

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.