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ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India
Corona Virus Disease 2019 (COVID-19) is a continuing extensive incident globally affecting several million people's health and sometimes leading to death. The outbreak prediction and making cautious steps is the only way to prevent the spread of COVID-19. This paper presents an Adaptive Neuro-f...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452449/ https://www.ncbi.nlm.nih.gov/pubmed/34566264 http://dx.doi.org/10.1007/s00521-021-06412-w |
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author | Kumar, Rajagopal Al-Turjman, Fadi Srinivas, L. N. B. Braveen, M. Ramakrishnan, Jothilakshmi |
author_facet | Kumar, Rajagopal Al-Turjman, Fadi Srinivas, L. N. B. Braveen, M. Ramakrishnan, Jothilakshmi |
author_sort | Kumar, Rajagopal |
collection | PubMed |
description | Corona Virus Disease 2019 (COVID-19) is a continuing extensive incident globally affecting several million people's health and sometimes leading to death. The outbreak prediction and making cautious steps is the only way to prevent the spread of COVID-19. This paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS)-based machine learning technique to predict the possible outbreak in India. The proposed ANFIS-based prediction system tracks the growth of epidemic based on the previous data sets fetched from cloud computing. The proposed ANFIS technique predicts the epidemic peak and COVID-19 infected cases through the cloud data sets. The ANFIS is chosen for this study as it has both numerical and linguistic knowledge, and also has ability to classify data and identify patterns. The proposed technique not only predicts the outbreak but also tracks the disease and suggests a measurable policy to manage the COVID-19 epidemic. The obtained prediction shows that the proposed technique very effectively tracks the growth of the COVID-19 epidemic. The result shows the growth of infection rate decreases at end of 2020 and also has delay epidemic peak by 40–60 days. The prediction result using the proposed ANFIS technique shows a low Mean Square Error (MSE) of 1.184 × 10(–3) with an accuracy of 86%. The study provides important information for public health providers and the government to control the COVID-19 epidemic. |
format | Online Article Text |
id | pubmed-8452449 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-84524492021-09-21 ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India Kumar, Rajagopal Al-Turjman, Fadi Srinivas, L. N. B. Braveen, M. Ramakrishnan, Jothilakshmi Neural Comput Appl S.I. : Neuro, fuzzy and their Hybridization Corona Virus Disease 2019 (COVID-19) is a continuing extensive incident globally affecting several million people's health and sometimes leading to death. The outbreak prediction and making cautious steps is the only way to prevent the spread of COVID-19. This paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS)-based machine learning technique to predict the possible outbreak in India. The proposed ANFIS-based prediction system tracks the growth of epidemic based on the previous data sets fetched from cloud computing. The proposed ANFIS technique predicts the epidemic peak and COVID-19 infected cases through the cloud data sets. The ANFIS is chosen for this study as it has both numerical and linguistic knowledge, and also has ability to classify data and identify patterns. The proposed technique not only predicts the outbreak but also tracks the disease and suggests a measurable policy to manage the COVID-19 epidemic. The obtained prediction shows that the proposed technique very effectively tracks the growth of the COVID-19 epidemic. The result shows the growth of infection rate decreases at end of 2020 and also has delay epidemic peak by 40–60 days. The prediction result using the proposed ANFIS technique shows a low Mean Square Error (MSE) of 1.184 × 10(–3) with an accuracy of 86%. The study provides important information for public health providers and the government to control the COVID-19 epidemic. Springer London 2021-09-21 2023 /pmc/articles/PMC8452449/ /pubmed/34566264 http://dx.doi.org/10.1007/s00521-021-06412-w Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | S.I. : Neuro, fuzzy and their Hybridization Kumar, Rajagopal Al-Turjman, Fadi Srinivas, L. N. B. Braveen, M. Ramakrishnan, Jothilakshmi ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India |
title | ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India |
title_full | ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India |
title_fullStr | ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India |
title_full_unstemmed | ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India |
title_short | ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India |
title_sort | anfis for prediction of epidemic peak and infected cases for covid-19 in india |
topic | S.I. : Neuro, fuzzy and their Hybridization |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452449/ https://www.ncbi.nlm.nih.gov/pubmed/34566264 http://dx.doi.org/10.1007/s00521-021-06412-w |
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