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Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system
Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity’s body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was dev...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8004563/ https://www.ncbi.nlm.nih.gov/pubmed/33814730 http://dx.doi.org/10.1007/s00530-021-00774-w |
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author | Iwendi, Celestine Mahboob, Kainaat Khalid, Zarnab Javed, Abdul Rehman Rizwan, Muhammad Ghosh, Uttam |
author_facet | Iwendi, Celestine Mahboob, Kainaat Khalid, Zarnab Javed, Abdul Rehman Rizwan, Muhammad Ghosh, Uttam |
author_sort | Iwendi, Celestine |
collection | PubMed |
description | Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity’s body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease’s risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19. |
format | Online Article Text |
id | pubmed-8004563 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-80045632021-03-29 Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system Iwendi, Celestine Mahboob, Kainaat Khalid, Zarnab Javed, Abdul Rehman Rizwan, Muhammad Ghosh, Uttam Multimed Syst Special Issue Paper Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity’s body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease’s risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19. Springer Berlin Heidelberg 2021-03-28 2022 /pmc/articles/PMC8004563/ /pubmed/33814730 http://dx.doi.org/10.1007/s00530-021-00774-w Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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 | Special Issue Paper Iwendi, Celestine Mahboob, Kainaat Khalid, Zarnab Javed, Abdul Rehman Rizwan, Muhammad Ghosh, Uttam Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system |
title | Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system |
title_full | Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system |
title_fullStr | Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system |
title_full_unstemmed | Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system |
title_short | Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system |
title_sort | classification of covid-19 individuals using adaptive neuro-fuzzy inference system |
topic | Special Issue Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8004563/ https://www.ncbi.nlm.nih.gov/pubmed/33814730 http://dx.doi.org/10.1007/s00530-021-00774-w |
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