Ensemble learning for multi-class COVID-19 detection from big data.
Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data scie...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0292587 |
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| _version_ | 1850203305740861440 |
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| author | Sarah Kaleem Adnan Sohail Muhammad Usman Tariq Muhammad Babar Basit Qureshi |
| author_facet | Sarah Kaleem Adnan Sohail Muhammad Usman Tariq Muhammad Babar Basit Qureshi |
| author_sort | Sarah Kaleem |
| collection | DOAJ |
| description | Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19. One cost-effective approach to detect the disease is the use of chest X-rays, which is a common initial testing method. Although existing techniques are useful for detecting COVID-19 using X-rays, there is a need for further improvement in efficiency, particularly in terms of training and execution time. This article introduces an advanced architecture that leverages an ensemble learning technique for COVID-19 detection from chest X-ray images. Using a parallel and distributed framework, the proposed model integrates ensemble learning with big data analytics to facilitate parallel processing. This approach aims to enhance both execution and training times, ensuring a more effective detection process. The model's efficacy was validated through a comprehensive analysis of predicted and actual values, and its performance was meticulously evaluated for accuracy, precision, recall, and F-measure, and compared to state-of-the-art models. The work presented here not only contributes to the ongoing fight against COVID-19 but also showcases the wider applicability and potential of ensemble learning techniques in healthcare. |
| format | Article |
| id | doaj-art-3bfbe0aedfb44b0881491b701aa3dd90 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-3bfbe0aedfb44b0881491b701aa3dd902025-08-20T02:11:34ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-011810e029258710.1371/journal.pone.0292587Ensemble learning for multi-class COVID-19 detection from big data.Sarah KaleemAdnan SohailMuhammad Usman TariqMuhammad BabarBasit QureshiCoronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19. One cost-effective approach to detect the disease is the use of chest X-rays, which is a common initial testing method. Although existing techniques are useful for detecting COVID-19 using X-rays, there is a need for further improvement in efficiency, particularly in terms of training and execution time. This article introduces an advanced architecture that leverages an ensemble learning technique for COVID-19 detection from chest X-ray images. Using a parallel and distributed framework, the proposed model integrates ensemble learning with big data analytics to facilitate parallel processing. This approach aims to enhance both execution and training times, ensuring a more effective detection process. The model's efficacy was validated through a comprehensive analysis of predicted and actual values, and its performance was meticulously evaluated for accuracy, precision, recall, and F-measure, and compared to state-of-the-art models. The work presented here not only contributes to the ongoing fight against COVID-19 but also showcases the wider applicability and potential of ensemble learning techniques in healthcare.https://doi.org/10.1371/journal.pone.0292587 |
| spellingShingle | Sarah Kaleem Adnan Sohail Muhammad Usman Tariq Muhammad Babar Basit Qureshi Ensemble learning for multi-class COVID-19 detection from big data. PLoS ONE |
| title | Ensemble learning for multi-class COVID-19 detection from big data. |
| title_full | Ensemble learning for multi-class COVID-19 detection from big data. |
| title_fullStr | Ensemble learning for multi-class COVID-19 detection from big data. |
| title_full_unstemmed | Ensemble learning for multi-class COVID-19 detection from big data. |
| title_short | Ensemble learning for multi-class COVID-19 detection from big data. |
| title_sort | ensemble learning for multi class covid 19 detection from big data |
| url | https://doi.org/10.1371/journal.pone.0292587 |
| work_keys_str_mv | AT sarahkaleem ensemblelearningformulticlasscovid19detectionfrombigdata AT adnansohail ensemblelearningformulticlasscovid19detectionfrombigdata AT muhammadusmantariq ensemblelearningformulticlasscovid19detectionfrombigdata AT muhammadbabar ensemblelearningformulticlasscovid19detectionfrombigdata AT basitqureshi ensemblelearningformulticlasscovid19detectionfrombigdata |