Use of CatBoost algorithm to identify the need for surgery in infants with necrotizing enterocolitis
BackgroundEarly identification of infants with necrotizing enterocolitis (NEC) at risk of surgery is essential for an effective treatment. This study aims to clarify the risk factors of surgical NEC and establish a prediction model by machine learning algorithm.MethodsInfants with NEC were split int...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Pediatrics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2025.1465278/full |
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| author | Xinyun Jin Xinyun Jin Wenqiang Sun Wenqiang Sun Yihui Li Yihui Li Yinglin Su Lingqi Xu Lingqi Xu Xueping Zhu |
| author_facet | Xinyun Jin Xinyun Jin Wenqiang Sun Wenqiang Sun Yihui Li Yihui Li Yinglin Su Lingqi Xu Lingqi Xu Xueping Zhu |
| author_sort | Xinyun Jin |
| collection | DOAJ |
| description | BackgroundEarly identification of infants with necrotizing enterocolitis (NEC) at risk of surgery is essential for an effective treatment. This study aims to clarify the risk factors of surgical NEC and establish a prediction model by machine learning algorithm.MethodsInfants with NEC were split into two groups based on whether they had surgery or not. Clinical data was collected and compared between the groups. Variables were analyzed with one-way logistic regression and predictive models were built using logistic regression and CatBoost algorithm. The models were evaluated and compared using Receiver Operating Characteristic (ROC) curves and feature importance. Feature importance was ranked using the SHapley Additive exPlanation method and model optimization was performed using feature culling. Final model was selected and a user-friendly GUI software was created for clinical use.ResultsThe Catboost model performed better than the logistic regression model in terms of discriminative power. An interpretable final model with 14 features was built after the features were reduced according to the feature importance level. The final model accurately identified Surgicel NEC in the internal validation (AUC = 0.905) and was translated into a convenient tool to facilitate its use in clinical settings.ConclusionsCatboost machine learning model related to infants with surgical NEC was successfully developed. A GUI interface was developed to assist clinicians in accurately identifying children who would benefit from surgery. |
| format | Article |
| id | doaj-art-a7939e3349aa4d5c9584cc2efdfee72b |
| institution | OA Journals |
| issn | 2296-2360 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Pediatrics |
| spelling | doaj-art-a7939e3349aa4d5c9584cc2efdfee72b2025-08-20T02:14:38ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602025-02-011310.3389/fped.2025.14652781465278Use of CatBoost algorithm to identify the need for surgery in infants with necrotizing enterocolitisXinyun Jin0Xinyun Jin1Wenqiang Sun2Wenqiang Sun3Yihui Li4Yihui Li5Yinglin Su6Lingqi Xu7Lingqi Xu8Xueping Zhu9Department of Neonatology, Children’s Hospital of Soochow University, Suzhou, ChinaClinical Pediatrics School, Soochow University, Suzhou, ChinaDepartment of Neonatology, Children’s Hospital of Soochow University, Suzhou, ChinaClinical Pediatrics School, Soochow University, Suzhou, ChinaDepartment of Neonatology, Children’s Hospital of Soochow University, Suzhou, ChinaClinical Pediatrics School, Soochow University, Suzhou, ChinaDepartment of Neonatology, Wuxi Children’s Hospital, Wuxi, ChinaDepartment of Neonatology, Children’s Hospital of Soochow University, Suzhou, ChinaClinical Pediatrics School, Soochow University, Suzhou, ChinaDepartment of Neonatology, Children’s Hospital of Soochow University, Suzhou, ChinaBackgroundEarly identification of infants with necrotizing enterocolitis (NEC) at risk of surgery is essential for an effective treatment. This study aims to clarify the risk factors of surgical NEC and establish a prediction model by machine learning algorithm.MethodsInfants with NEC were split into two groups based on whether they had surgery or not. Clinical data was collected and compared between the groups. Variables were analyzed with one-way logistic regression and predictive models were built using logistic regression and CatBoost algorithm. The models were evaluated and compared using Receiver Operating Characteristic (ROC) curves and feature importance. Feature importance was ranked using the SHapley Additive exPlanation method and model optimization was performed using feature culling. Final model was selected and a user-friendly GUI software was created for clinical use.ResultsThe Catboost model performed better than the logistic regression model in terms of discriminative power. An interpretable final model with 14 features was built after the features were reduced according to the feature importance level. The final model accurately identified Surgicel NEC in the internal validation (AUC = 0.905) and was translated into a convenient tool to facilitate its use in clinical settings.ConclusionsCatboost machine learning model related to infants with surgical NEC was successfully developed. A GUI interface was developed to assist clinicians in accurately identifying children who would benefit from surgery.https://www.frontiersin.org/articles/10.3389/fped.2025.1465278/fullnecrotizing enterocolitissurgical NECrisk factorsCatBoost machine learning modelGUI interface |
| spellingShingle | Xinyun Jin Xinyun Jin Wenqiang Sun Wenqiang Sun Yihui Li Yihui Li Yinglin Su Lingqi Xu Lingqi Xu Xueping Zhu Use of CatBoost algorithm to identify the need for surgery in infants with necrotizing enterocolitis Frontiers in Pediatrics necrotizing enterocolitis surgical NEC risk factors CatBoost machine learning model GUI interface |
| title | Use of CatBoost algorithm to identify the need for surgery in infants with necrotizing enterocolitis |
| title_full | Use of CatBoost algorithm to identify the need for surgery in infants with necrotizing enterocolitis |
| title_fullStr | Use of CatBoost algorithm to identify the need for surgery in infants with necrotizing enterocolitis |
| title_full_unstemmed | Use of CatBoost algorithm to identify the need for surgery in infants with necrotizing enterocolitis |
| title_short | Use of CatBoost algorithm to identify the need for surgery in infants with necrotizing enterocolitis |
| title_sort | use of catboost algorithm to identify the need for surgery in infants with necrotizing enterocolitis |
| topic | necrotizing enterocolitis surgical NEC risk factors CatBoost machine learning model GUI interface |
| url | https://www.frontiersin.org/articles/10.3389/fped.2025.1465278/full |
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