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Ensemble learning-based early detection of influenza disease
Across the world, the seasonal disease influenza is a respiratory illness that impacts all age groups in many ways. Its symptoms are fever, chills, aches, pains, headaches, fatigue, cough, and weakness. Seasonal influenza can cause mild to severe illness and lead to death at times. The task of early...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199437/ https://www.ncbi.nlm.nih.gov/pubmed/37362719 http://dx.doi.org/10.1007/s11042-023-15848-2 |
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author | Kumar, Ranjan Maheshwari, Sajal Sharma, Anushka Linda, Sonal Kumar, Subhash Chatterjee, Indranath |
author_facet | Kumar, Ranjan Maheshwari, Sajal Sharma, Anushka Linda, Sonal Kumar, Subhash Chatterjee, Indranath |
author_sort | Kumar, Ranjan |
collection | PubMed |
description | Across the world, the seasonal disease influenza is a respiratory illness that impacts all age groups in many ways. Its symptoms are fever, chills, aches, pains, headaches, fatigue, cough, and weakness. Seasonal influenza can cause mild to severe illness and lead to death at times. The task of early detection of influenza is an important research area these days. Various studies show that machine learning techniques have attracted many researchers' attention to the early detection of influenza disease. In this paper, early detection of Influenza disease among all age groups is done using various machine learning techniques. Influenza Research Database and the Human Surveillance Records data sets are used. Data analysis is undertaken, and ensemble-based stacked algorithms are implemented on the whole data set. The performance of different models has been evaluated using different performance metrics. Overall, the study proposes efficient machine learning models that can be implemented to provide a cheaper and quicker diagnostic tool for detecting influenza. |
format | Online Article Text |
id | pubmed-10199437 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-101994372023-05-23 Ensemble learning-based early detection of influenza disease Kumar, Ranjan Maheshwari, Sajal Sharma, Anushka Linda, Sonal Kumar, Subhash Chatterjee, Indranath Multimed Tools Appl Article Across the world, the seasonal disease influenza is a respiratory illness that impacts all age groups in many ways. Its symptoms are fever, chills, aches, pains, headaches, fatigue, cough, and weakness. Seasonal influenza can cause mild to severe illness and lead to death at times. The task of early detection of influenza is an important research area these days. Various studies show that machine learning techniques have attracted many researchers' attention to the early detection of influenza disease. In this paper, early detection of Influenza disease among all age groups is done using various machine learning techniques. Influenza Research Database and the Human Surveillance Records data sets are used. Data analysis is undertaken, and ensemble-based stacked algorithms are implemented on the whole data set. The performance of different models has been evaluated using different performance metrics. Overall, the study proposes efficient machine learning models that can be implemented to provide a cheaper and quicker diagnostic tool for detecting influenza. Springer US 2023-05-20 /pmc/articles/PMC10199437/ /pubmed/37362719 http://dx.doi.org/10.1007/s11042-023-15848-2 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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 | Article Kumar, Ranjan Maheshwari, Sajal Sharma, Anushka Linda, Sonal Kumar, Subhash Chatterjee, Indranath Ensemble learning-based early detection of influenza disease |
title | Ensemble learning-based early detection of influenza disease |
title_full | Ensemble learning-based early detection of influenza disease |
title_fullStr | Ensemble learning-based early detection of influenza disease |
title_full_unstemmed | Ensemble learning-based early detection of influenza disease |
title_short | Ensemble learning-based early detection of influenza disease |
title_sort | ensemble learning-based early detection of influenza disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199437/ https://www.ncbi.nlm.nih.gov/pubmed/37362719 http://dx.doi.org/10.1007/s11042-023-15848-2 |
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