Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods
Abstract COVID-19 has posed a significant global health challenge, affecting individuals across all age groups. While extensive research has focused on adults, pediatric patients exhibit distinct clinical characteristics that necessitate specialized predictive models for disease severity. Machine le...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-15366-1 |
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| author | Babak Pourakbari Setareh Mamishi Sepideh Keshavarz Valian Shima Mahmoudi Reihaneh Hosseinpour Sadeghi Mohammad Reza Abdolsalehi Mahmoud Khodabandeh Mohammad Farahmand |
| author_facet | Babak Pourakbari Setareh Mamishi Sepideh Keshavarz Valian Shima Mahmoudi Reihaneh Hosseinpour Sadeghi Mohammad Reza Abdolsalehi Mahmoud Khodabandeh Mohammad Farahmand |
| author_sort | Babak Pourakbari |
| collection | DOAJ |
| description | Abstract COVID-19 has posed a significant global health challenge, affecting individuals across all age groups. While extensive research has focused on adults, pediatric patients exhibit distinct clinical characteristics that necessitate specialized predictive models for disease severity. Machine learning offers a powerful approach to analyzing complex datasets and predicting outcomes, yet its application in pediatric COVID-19 remains limited. This study evaluates the performance of machine learning algorithms in predicting disease severity among pediatrics. A retrospective analysis was conducted on a dataset of 588 pediatric with confirmed COVID-19, incorporating demographic, clinical, and laboratory variables. Various machine learning models were trained and assessed, with a SuperLearner ensemble model implemented to enhance predictive accuracy. Among the models, Random Forest exhibited the highest performance, achieving an accuracy of 90.1%, sensitivity of 90.2%, and specificity of 90.1%. The SuperLearner ensemble further improved predictive performance, demonstrating the lowest mean risk estimate. Key predictors, including oxygen saturation, respiratory parameters, and specific laboratory markers, played a crucial role in distinguishing severe from non-severe cases. These findings emphasize the potential of machine learning, particularly ensemble methods, in improving risk stratification for pediatric COVID-19. Integrating these predictive models into clinical practice could support early identification of high-risk patients and optimize clinical decision-making. |
| format | Article |
| id | doaj-art-ca8c59ab384c4f8799fbc29edcdbeb40 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ca8c59ab384c4f8799fbc29edcdbeb402025-08-20T04:02:46ZengNature PortfolioScientific Reports2045-23222025-08-0115111510.1038/s41598-025-15366-1Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methodsBabak Pourakbari0Setareh Mamishi1Sepideh Keshavarz Valian2Shima Mahmoudi3Reihaneh Hosseinpour Sadeghi4Mohammad Reza Abdolsalehi5Mahmoud Khodabandeh6Mohammad Farahmand7Pediatric Infectious Disease Research Center, Tehran University of Medical SciencesPediatric Infectious Disease Research Center, Tehran University of Medical SciencesSchool of Medicine, Tehran University of Medical SciencesBiotechnology Centre, Silesian University of TechnologyPediatric Infectious Disease Research Center, Tehran University of Medical SciencesDepartment of Infectious Diseases, Pediatrics Center of Excellence, Children’s Medical Center, Tehran University of Medical SciencesDepartment of Infectious Diseases, Pediatrics Center of Excellence, Children’s Medical Center, Tehran University of Medical SciencesPediatric Infectious Disease Research Center, Tehran University of Medical SciencesAbstract COVID-19 has posed a significant global health challenge, affecting individuals across all age groups. While extensive research has focused on adults, pediatric patients exhibit distinct clinical characteristics that necessitate specialized predictive models for disease severity. Machine learning offers a powerful approach to analyzing complex datasets and predicting outcomes, yet its application in pediatric COVID-19 remains limited. This study evaluates the performance of machine learning algorithms in predicting disease severity among pediatrics. A retrospective analysis was conducted on a dataset of 588 pediatric with confirmed COVID-19, incorporating demographic, clinical, and laboratory variables. Various machine learning models were trained and assessed, with a SuperLearner ensemble model implemented to enhance predictive accuracy. Among the models, Random Forest exhibited the highest performance, achieving an accuracy of 90.1%, sensitivity of 90.2%, and specificity of 90.1%. The SuperLearner ensemble further improved predictive performance, demonstrating the lowest mean risk estimate. Key predictors, including oxygen saturation, respiratory parameters, and specific laboratory markers, played a crucial role in distinguishing severe from non-severe cases. These findings emphasize the potential of machine learning, particularly ensemble methods, in improving risk stratification for pediatric COVID-19. Integrating these predictive models into clinical practice could support early identification of high-risk patients and optimize clinical decision-making.https://doi.org/10.1038/s41598-025-15366-1COVID-19Pediatric patientsMachine learningSeverity predictionRandom forestEnsemble model |
| spellingShingle | Babak Pourakbari Setareh Mamishi Sepideh Keshavarz Valian Shima Mahmoudi Reihaneh Hosseinpour Sadeghi Mohammad Reza Abdolsalehi Mahmoud Khodabandeh Mohammad Farahmand Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods Scientific Reports COVID-19 Pediatric patients Machine learning Severity prediction Random forest Ensemble model |
| title | Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods |
| title_full | Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods |
| title_fullStr | Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods |
| title_full_unstemmed | Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods |
| title_short | Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods |
| title_sort | predicting covid 19 severity in pediatric patients using machine learning a comparative analysis of algorithms and ensemble methods |
| topic | COVID-19 Pediatric patients Machine learning Severity prediction Random forest Ensemble model |
| url | https://doi.org/10.1038/s41598-025-15366-1 |
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