Prediction models based on machine learning algorithms for COVID-19 severity risk
Abstract Background The World Health Organization has highlighted the risk of Disease X, urging pandemic preparedness. Coronavirus disease 2019 (COVID-19) could be the first Disease X; therefore, understanding the epidemiological experiences of COVID-19 is crucial while preparing for future similar...
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| Format: | Article |
| Language: | English |
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BMC
2025-05-01
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| Series: | BMC Public Health |
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| Online Access: | https://doi.org/10.1186/s12889-025-22976-x |
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| author | Hansong Zhang Ying Wang Yan Xie Cuihan Wang Yuqi Ma Xin Jin |
| author_facet | Hansong Zhang Ying Wang Yan Xie Cuihan Wang Yuqi Ma Xin Jin |
| author_sort | Hansong Zhang |
| collection | DOAJ |
| description | Abstract Background The World Health Organization has highlighted the risk of Disease X, urging pandemic preparedness. Coronavirus disease 2019 (COVID-19) could be the first Disease X; therefore, understanding the epidemiological experiences of COVID-19 is crucial while preparing for future similar diseases. Methods Prediction models for COVID-19 severity risk in hospitalized patients were constructed based on four machine learning algorithms, namely, logistic regression, Cox regression, support vector machine (SVM), and random forest. These models were evaluated for prediction accuracy, area under the curve (AUC), sensitivity, and specificity as well as were interpreted using SHapley Additive exPlanation. Results Data were collected from 1,485 hospitalized patients across 6 centers, comprising 1,184 patients with severe or critical COVID-19 and 301 patients with nonsevere COVID-19. Among the four models, the SVM model achieved the highest prediction accuracy of 98.45%, with an AUC of 0.994, a sensitivity of 0.989, and a specificity of 0.969. Moreover, oxygenation index (OI), confusion, respiratory rate, and age were found to be predictors of COVID-19 severity risk. Conclusions SVM could accurately predict COVID-19 severity risk; thus, it can be prioritized as a prediction model. OI is the most critical predictor of COVID-19 severity risk and can serve as the primary and independent evaluation indicator. |
| format | Article |
| id | doaj-art-d0809406ec2c469fbdcdfc107b1c02f7 |
| institution | DOAJ |
| issn | 1471-2458 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj-art-d0809406ec2c469fbdcdfc107b1c02f72025-08-20T03:07:54ZengBMCBMC Public Health1471-24582025-05-0125111910.1186/s12889-025-22976-xPrediction models based on machine learning algorithms for COVID-19 severity riskHansong Zhang0Ying Wang1Yan Xie2Cuihan Wang3Yuqi Ma4Xin Jin5School of Mechanical Engineering, Tianjin UniversityDepartment of Nursing, Tianjin First Center HospitalDepartment of Liver Transplantation, Tianjin First Center HospitalTianjin Nankai Hospital, Tianjin Medical UniversitySchool of Mechanical Engineering, Tianjin UniversityMedical School of Tianjin UniversityAbstract Background The World Health Organization has highlighted the risk of Disease X, urging pandemic preparedness. Coronavirus disease 2019 (COVID-19) could be the first Disease X; therefore, understanding the epidemiological experiences of COVID-19 is crucial while preparing for future similar diseases. Methods Prediction models for COVID-19 severity risk in hospitalized patients were constructed based on four machine learning algorithms, namely, logistic regression, Cox regression, support vector machine (SVM), and random forest. These models were evaluated for prediction accuracy, area under the curve (AUC), sensitivity, and specificity as well as were interpreted using SHapley Additive exPlanation. Results Data were collected from 1,485 hospitalized patients across 6 centers, comprising 1,184 patients with severe or critical COVID-19 and 301 patients with nonsevere COVID-19. Among the four models, the SVM model achieved the highest prediction accuracy of 98.45%, with an AUC of 0.994, a sensitivity of 0.989, and a specificity of 0.969. Moreover, oxygenation index (OI), confusion, respiratory rate, and age were found to be predictors of COVID-19 severity risk. Conclusions SVM could accurately predict COVID-19 severity risk; thus, it can be prioritized as a prediction model. OI is the most critical predictor of COVID-19 severity risk and can serve as the primary and independent evaluation indicator.https://doi.org/10.1186/s12889-025-22976-xPrediction modelsMachine learning algorithmsSeverity riskCOVID-19 |
| spellingShingle | Hansong Zhang Ying Wang Yan Xie Cuihan Wang Yuqi Ma Xin Jin Prediction models based on machine learning algorithms for COVID-19 severity risk BMC Public Health Prediction models Machine learning algorithms Severity risk COVID-19 |
| title | Prediction models based on machine learning algorithms for COVID-19 severity risk |
| title_full | Prediction models based on machine learning algorithms for COVID-19 severity risk |
| title_fullStr | Prediction models based on machine learning algorithms for COVID-19 severity risk |
| title_full_unstemmed | Prediction models based on machine learning algorithms for COVID-19 severity risk |
| title_short | Prediction models based on machine learning algorithms for COVID-19 severity risk |
| title_sort | prediction models based on machine learning algorithms for covid 19 severity risk |
| topic | Prediction models Machine learning algorithms Severity risk COVID-19 |
| url | https://doi.org/10.1186/s12889-025-22976-x |
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