Advanced machine learning based exploration for predicting pandemic fatality: Oman dataset
Pandemic-causing pathogens as COVID-19 can lead to a range of symptoms in humans, which may include fever, breathing difficulties, fatigue, cough, and severe respiratory distress. In more serious cases, these pathogens can be fatal. This paper presents the outcomes of a cohort study of 467 confirmed...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914823002393 |
| _version_ | 1797401755088060416 |
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| author | Jamil Al Shaqsi Osama Drogham Sanad Aburass |
| author_facet | Jamil Al Shaqsi Osama Drogham Sanad Aburass |
| author_sort | Jamil Al Shaqsi |
| collection | DOAJ |
| description | Pandemic-causing pathogens as COVID-19 can lead to a range of symptoms in humans, which may include fever, breathing difficulties, fatigue, cough, and severe respiratory distress. In more serious cases, these pathogens can be fatal. This paper presents the outcomes of a cohort study of 467 confirmed cases of COVID-19 as a specific pandemic-causing pathogen in Oman. Machine Learning-algorithms were employed to extract the hidden patterns and identify the factors of death or survival from the obtained datasets. The 10-fold Cross Validation was applied to ensure the reliability of the results. The experimental results demonstrated that some parameters contribute significantly to the death of the infected patients. It has been revealed that, Sodium, Hemoglobin, Mean Cell Volume, Chloride, and Eosinophil are the most significant factors in predicting the progression of the disease and the final outcome. The findings also suggested that age, gender, chronic kidney disease, and other complete blood count parameters are risk factors for poor prognosis in older patients. The obtained results are promising as they give insight into the main causes of patient status: recovery and death. |
| first_indexed | 2024-03-09T02:14:41Z |
| format | Article |
| id | doaj.art-6aacfbd8d2e64a60b911bb0d5be68e1f |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-03-09T02:14:41Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-6aacfbd8d2e64a60b911bb0d5be68e1f2023-12-07T05:29:13ZengElsevierInformatics in Medicine Unlocked2352-91482023-01-0143101393Advanced machine learning based exploration for predicting pandemic fatality: Oman datasetJamil Al Shaqsi0Osama Drogham1Sanad Aburass2Information Systems Department, Sultan Qaboos University, Oman; Corresponding author.Prince Abdullah bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, 19117, Al-Salt, Jordan; School of Information Technology, Skyline University College, University City of Sharjah, P.O. Box 1797, Sharjah, United Arab EmiratesDepartment of Computer Science, Maharishi International University, Fairfield, IA, USAPandemic-causing pathogens as COVID-19 can lead to a range of symptoms in humans, which may include fever, breathing difficulties, fatigue, cough, and severe respiratory distress. In more serious cases, these pathogens can be fatal. This paper presents the outcomes of a cohort study of 467 confirmed cases of COVID-19 as a specific pandemic-causing pathogen in Oman. Machine Learning-algorithms were employed to extract the hidden patterns and identify the factors of death or survival from the obtained datasets. The 10-fold Cross Validation was applied to ensure the reliability of the results. The experimental results demonstrated that some parameters contribute significantly to the death of the infected patients. It has been revealed that, Sodium, Hemoglobin, Mean Cell Volume, Chloride, and Eosinophil are the most significant factors in predicting the progression of the disease and the final outcome. The findings also suggested that age, gender, chronic kidney disease, and other complete blood count parameters are risk factors for poor prognosis in older patients. The obtained results are promising as they give insight into the main causes of patient status: recovery and death.http://www.sciencedirect.com/science/article/pii/S2352914823002393COVID-19PandemicMachine learningFeature selectionKnowledge discoveryBlood parameters |
| spellingShingle | Jamil Al Shaqsi Osama Drogham Sanad Aburass Advanced machine learning based exploration for predicting pandemic fatality: Oman dataset Informatics in Medicine Unlocked COVID-19 Pandemic Machine learning Feature selection Knowledge discovery Blood parameters |
| title | Advanced machine learning based exploration for predicting pandemic fatality: Oman dataset |
| title_full | Advanced machine learning based exploration for predicting pandemic fatality: Oman dataset |
| title_fullStr | Advanced machine learning based exploration for predicting pandemic fatality: Oman dataset |
| title_full_unstemmed | Advanced machine learning based exploration for predicting pandemic fatality: Oman dataset |
| title_short | Advanced machine learning based exploration for predicting pandemic fatality: Oman dataset |
| title_sort | advanced machine learning based exploration for predicting pandemic fatality oman dataset |
| topic | COVID-19 Pandemic Machine learning Feature selection Knowledge discovery Blood parameters |
| url | http://www.sciencedirect.com/science/article/pii/S2352914823002393 |
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