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Artificial intelligence for diagnosis of mild–moderate COVID-19 using haematological markers
OBJECTIVE: The persistent spread of SARS-CoV-2 makes diagnosis challenging because COVID-19 symptoms are hard to differentiate from those of other respiratory illnesses. The reverse transcription-polymerase chain reaction test is the current golden standard for diagnosing various respiratory disease...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10339777/ https://www.ncbi.nlm.nih.gov/pubmed/37436038 http://dx.doi.org/10.1080/07853890.2023.2233541 |
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author | Chadaga, Krishnaraj Prabhu, Srikanth Bhat, Vivekananda Sampathila, Niranjana Umakanth, Shashikiran Chadaga, Rajagopala |
author_facet | Chadaga, Krishnaraj Prabhu, Srikanth Bhat, Vivekananda Sampathila, Niranjana Umakanth, Shashikiran Chadaga, Rajagopala |
author_sort | Chadaga, Krishnaraj |
collection | PubMed |
description | OBJECTIVE: The persistent spread of SARS-CoV-2 makes diagnosis challenging because COVID-19 symptoms are hard to differentiate from those of other respiratory illnesses. The reverse transcription-polymerase chain reaction test is the current golden standard for diagnosing various respiratory diseases, including COVID-19. However, this standard diagnostic method is prone to erroneous and false negative results (10% -15%). Therefore, finding an alternative technique to validate the RT-PCR test is paramount. Artificial intelligence (AI) and machine learning (ML) applications are extensively used in medical research. Hence, this study focused on developing a decision support system using AI to diagnose mild-moderate COVID-19 from other similar diseases using demographic and clinical markers. Severe COVID-19 cases were not considered in this study since fatality rates have dropped considerably after introducing COVID-19 vaccines. METHODS: A custom stacked ensemble model consisting of various heterogeneous algorithms has been utilized for prediction. Four deep learning algorithms have also been tested and compared, such as one-dimensional convolutional neural networks, long short-term memory networks, deep neural networks and Residual Multi-Layer Perceptron. Five explainers, namely, Shapley Additive Values, Eli5, QLattice, Anchor and Local Interpretable Model-agnostic Explanations, have been utilized to interpret the predictions made by the classifiers. RESULTS: After using Pearson’s correlation and particle swarm optimization feature selection, the final stack obtained a maximum accuracy of 89%. The most important markers which were useful in COVID-19 diagnosis are Eosinophil, Albumin, T. Bilirubin, ALP, ALT, AST, HbA1c and TWBC. CONCLUSION: The promising results suggest using this decision support system to diagnose COVID-19 from other similar respiratory illnesses. |
format | Online Article Text |
id | pubmed-10339777 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-103397772023-07-14 Artificial intelligence for diagnosis of mild–moderate COVID-19 using haematological markers Chadaga, Krishnaraj Prabhu, Srikanth Bhat, Vivekananda Sampathila, Niranjana Umakanth, Shashikiran Chadaga, Rajagopala Ann Med Infectious Diseases OBJECTIVE: The persistent spread of SARS-CoV-2 makes diagnosis challenging because COVID-19 symptoms are hard to differentiate from those of other respiratory illnesses. The reverse transcription-polymerase chain reaction test is the current golden standard for diagnosing various respiratory diseases, including COVID-19. However, this standard diagnostic method is prone to erroneous and false negative results (10% -15%). Therefore, finding an alternative technique to validate the RT-PCR test is paramount. Artificial intelligence (AI) and machine learning (ML) applications are extensively used in medical research. Hence, this study focused on developing a decision support system using AI to diagnose mild-moderate COVID-19 from other similar diseases using demographic and clinical markers. Severe COVID-19 cases were not considered in this study since fatality rates have dropped considerably after introducing COVID-19 vaccines. METHODS: A custom stacked ensemble model consisting of various heterogeneous algorithms has been utilized for prediction. Four deep learning algorithms have also been tested and compared, such as one-dimensional convolutional neural networks, long short-term memory networks, deep neural networks and Residual Multi-Layer Perceptron. Five explainers, namely, Shapley Additive Values, Eli5, QLattice, Anchor and Local Interpretable Model-agnostic Explanations, have been utilized to interpret the predictions made by the classifiers. RESULTS: After using Pearson’s correlation and particle swarm optimization feature selection, the final stack obtained a maximum accuracy of 89%. The most important markers which were useful in COVID-19 diagnosis are Eosinophil, Albumin, T. Bilirubin, ALP, ALT, AST, HbA1c and TWBC. CONCLUSION: The promising results suggest using this decision support system to diagnose COVID-19 from other similar respiratory illnesses. Taylor & Francis 2023-07-12 /pmc/articles/PMC10339777/ /pubmed/37436038 http://dx.doi.org/10.1080/07853890.2023.2233541 Text en © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. |
spellingShingle | Infectious Diseases Chadaga, Krishnaraj Prabhu, Srikanth Bhat, Vivekananda Sampathila, Niranjana Umakanth, Shashikiran Chadaga, Rajagopala Artificial intelligence for diagnosis of mild–moderate COVID-19 using haematological markers |
title | Artificial intelligence for diagnosis of mild–moderate COVID-19 using haematological markers |
title_full | Artificial intelligence for diagnosis of mild–moderate COVID-19 using haematological markers |
title_fullStr | Artificial intelligence for diagnosis of mild–moderate COVID-19 using haematological markers |
title_full_unstemmed | Artificial intelligence for diagnosis of mild–moderate COVID-19 using haematological markers |
title_short | Artificial intelligence for diagnosis of mild–moderate COVID-19 using haematological markers |
title_sort | artificial intelligence for diagnosis of mild–moderate covid-19 using haematological markers |
topic | Infectious Diseases |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10339777/ https://www.ncbi.nlm.nih.gov/pubmed/37436038 http://dx.doi.org/10.1080/07853890.2023.2233541 |
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