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A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients
Harris’ Hawk Optimization (HHO) is a novel metaheuristic inspired by the collective hunting behaviors of hawks. This technique employs the flight patterns of hawks to produce (near)-optimal solutions, enhanced with feature selection, for challenging classification problems. In this study, we propose...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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PeerJ Inc.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280461/ https://www.ncbi.nlm.nih.gov/pubmed/37346714 http://dx.doi.org/10.7717/peerj-cs.1430 |
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author | Dokeroglu, Tansel |
author_facet | Dokeroglu, Tansel |
author_sort | Dokeroglu, Tansel |
collection | PubMed |
description | Harris’ Hawk Optimization (HHO) is a novel metaheuristic inspired by the collective hunting behaviors of hawks. This technique employs the flight patterns of hawks to produce (near)-optimal solutions, enhanced with feature selection, for challenging classification problems. In this study, we propose a new parallel multi-objective HHO algorithm for predicting the mortality risk of COVID-19 patients based on their symptoms. There are two objectives in this optimization problem: to reduce the number of features while increasing the accuracy of the predictions. We conduct comprehensive experiments on a recent real-world COVID-19 dataset from Kaggle. An augmented version of the COVID-19 dataset is also generated and experimentally shown to improve the quality of the solutions. Significant improvements are observed compared to existing state-of-the-art metaheuristic wrapper algorithms. We report better classification results with feature selection than when using the entire set of features. During experiments, a 98.15% prediction accuracy with a 45% reduction is achieved in the number of features. We successfully obtained new best solutions for this COVID-19 dataset. |
format | Online Article Text |
id | pubmed-10280461 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102804612023-06-21 A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients Dokeroglu, Tansel PeerJ Comput Sci Algorithms and Analysis of Algorithms Harris’ Hawk Optimization (HHO) is a novel metaheuristic inspired by the collective hunting behaviors of hawks. This technique employs the flight patterns of hawks to produce (near)-optimal solutions, enhanced with feature selection, for challenging classification problems. In this study, we propose a new parallel multi-objective HHO algorithm for predicting the mortality risk of COVID-19 patients based on their symptoms. There are two objectives in this optimization problem: to reduce the number of features while increasing the accuracy of the predictions. We conduct comprehensive experiments on a recent real-world COVID-19 dataset from Kaggle. An augmented version of the COVID-19 dataset is also generated and experimentally shown to improve the quality of the solutions. Significant improvements are observed compared to existing state-of-the-art metaheuristic wrapper algorithms. We report better classification results with feature selection than when using the entire set of features. During experiments, a 98.15% prediction accuracy with a 45% reduction is achieved in the number of features. We successfully obtained new best solutions for this COVID-19 dataset. PeerJ Inc. 2023-06-14 /pmc/articles/PMC10280461/ /pubmed/37346714 http://dx.doi.org/10.7717/peerj-cs.1430 Text en © 2023 Dokeroglu https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Algorithms and Analysis of Algorithms Dokeroglu, Tansel A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients |
title | A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients |
title_full | A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients |
title_fullStr | A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients |
title_full_unstemmed | A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients |
title_short | A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients |
title_sort | new parallel multi-objective harris hawk algorithm for predicting the mortality of covid-19 patients |
topic | Algorithms and Analysis of Algorithms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280461/ https://www.ncbi.nlm.nih.gov/pubmed/37346714 http://dx.doi.org/10.7717/peerj-cs.1430 |
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