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Deep forest model for diagnosing COVID-19 from routine blood tests
The Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and posed considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the limited quantity of swab tests, we propose a machine learning p...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371014/ https://www.ncbi.nlm.nih.gov/pubmed/34404838 http://dx.doi.org/10.1038/s41598-021-95957-w |
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author | AlJame, Maryam Imtiaz, Ayyub Ahmad, Imtiaz Mohammed, Ameer |
author_facet | AlJame, Maryam Imtiaz, Ayyub Ahmad, Imtiaz Mohammed, Ameer |
author_sort | AlJame, Maryam |
collection | PubMed |
description | The Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and posed considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the limited quantity of swab tests, we propose a machine learning prediction model to accurately diagnose COVID-19 from clinical and/or routine laboratory data. The model exploits a new ensemble-based method called the deep forest (DF), where multiple classifiers in multiple layers are used to encourage diversity and improve performance. The cascade level employs the layer-by-layer processing and is constructed from three different classifiers: extra trees, XGBoost, and LightGBM. The prediction model was trained and evaluated on two publicly available datasets. Experimental results show that the proposed DF model has an accuracy of 99.5%, sensitivity of 95.28%, and specificity of 99.96%. These performance metrics are comparable to other well-established machine learning techniques, and hence DF model can serve as a fast screening tool for COVID-19 patients at places where testing is scarce. |
format | Online Article Text |
id | pubmed-8371014 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83710142021-08-19 Deep forest model for diagnosing COVID-19 from routine blood tests AlJame, Maryam Imtiaz, Ayyub Ahmad, Imtiaz Mohammed, Ameer Sci Rep Article The Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and posed considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the limited quantity of swab tests, we propose a machine learning prediction model to accurately diagnose COVID-19 from clinical and/or routine laboratory data. The model exploits a new ensemble-based method called the deep forest (DF), where multiple classifiers in multiple layers are used to encourage diversity and improve performance. The cascade level employs the layer-by-layer processing and is constructed from three different classifiers: extra trees, XGBoost, and LightGBM. The prediction model was trained and evaluated on two publicly available datasets. Experimental results show that the proposed DF model has an accuracy of 99.5%, sensitivity of 95.28%, and specificity of 99.96%. These performance metrics are comparable to other well-established machine learning techniques, and hence DF model can serve as a fast screening tool for COVID-19 patients at places where testing is scarce. Nature Publishing Group UK 2021-08-17 /pmc/articles/PMC8371014/ /pubmed/34404838 http://dx.doi.org/10.1038/s41598-021-95957-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article AlJame, Maryam Imtiaz, Ayyub Ahmad, Imtiaz Mohammed, Ameer Deep forest model for diagnosing COVID-19 from routine blood tests |
title | Deep forest model for diagnosing COVID-19 from routine blood tests |
title_full | Deep forest model for diagnosing COVID-19 from routine blood tests |
title_fullStr | Deep forest model for diagnosing COVID-19 from routine blood tests |
title_full_unstemmed | Deep forest model for diagnosing COVID-19 from routine blood tests |
title_short | Deep forest model for diagnosing COVID-19 from routine blood tests |
title_sort | deep forest model for diagnosing covid-19 from routine blood tests |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371014/ https://www.ncbi.nlm.nih.gov/pubmed/34404838 http://dx.doi.org/10.1038/s41598-021-95957-w |
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