Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modeling
Abstract Purpose The distinction between complicated appendicitis (CAP) and uncomplicated appendicitis (UAP) remains challenging. The purpose of this study was to construct a safe and economical diagnostic model that can accurately and rapidly differentiate between CAP and UAP. Methods Patient data...
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BMC
2025-04-01
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| Series: | BMC Gastroenterology |
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| Online Access: | https://doi.org/10.1186/s12876-025-03847-6 |
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| author | Sunmeng Chen Jianfu Xia Beibei Xu Yi Huang Miaomiao Teng Juyi Pan |
| author_facet | Sunmeng Chen Jianfu Xia Beibei Xu Yi Huang Miaomiao Teng Juyi Pan |
| author_sort | Sunmeng Chen |
| collection | DOAJ |
| description | Abstract Purpose The distinction between complicated appendicitis (CAP) and uncomplicated appendicitis (UAP) remains challenging. The purpose of this study was to construct a safe and economical diagnostic model that can accurately and rapidly differentiate between CAP and UAP. Methods Patient data from 773 appendectomies were retrospectively collected, important features were selected using random forests, and the data were divided into training and test sets in a 3:1 ratio. An integrated learning algorithm, Extreme Gradient Boosting (XGBoost), was introduced to predict the risk of CAP and compared with Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (CART) algorithms. A comprehensive comparison of the four algorithms was performed using model performance metrics such as the area under the receiver’s operating characteristic curve (AUC), sensitivity, specificity, accuracy, precision, negative predictive value(NPV), positive predictive value(PPV),calibration curves, and clinical decision curve analysis (DCA). Result The results show that all four prediction models exhibit some predictive ability. The XGBoost model showed the best prediction with AUC, accuracy, sensitivity, specificity,NPV and PPV of 0.914, 0.855, 0.865, 0.846,0.848 and 0.897, respectively, followed by the SVM model with results of AUC, accuracy, sensitivity, specificity,NPV and PPV of 0.882, 0.819, 0.865, 0.779, 0.770 and 0.871, respectively. XGBoost and SVM models show very good calibration. The XGBoost model showed better net clinical benefit compared to the DCA curves of the other models. Conclusion Predictive models based on the XGBoost algorithm have good performance in predicting the risk of acute appendicitis progressing to complicated appendicitis, which helps to optimize clinical decision making. |
| format | Article |
| id | doaj-art-edd9e7ecb9f94741972ccabf817e615e |
| institution | DOAJ |
| issn | 1471-230X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Gastroenterology |
| spelling | doaj-art-edd9e7ecb9f94741972ccabf817e615e2025-08-20T03:14:06ZengBMCBMC Gastroenterology1471-230X2025-04-0125111210.1186/s12876-025-03847-6Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modelingSunmeng Chen0Jianfu Xia1Beibei Xu2Yi Huang3Miaomiao Teng4Juyi Pan5Department of General Surgery, The Dingli Clinical College of Wenzhou Medical University (Wenzhou Central Hospital)Department of General Surgery, The Dingli Clinical College of Wenzhou Medical University (Wenzhou Central Hospital)Department of Gastroenterology, Wenzhou Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai UniversityDepartment of General Surgery, Wenzhou Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai UniversityDepartment of Gastroenterology, Postgraduate Training Base Alliance of Wenzhou Medical UniversityDepartment of Gastroenterology, Wenzhou Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai UniversityAbstract Purpose The distinction between complicated appendicitis (CAP) and uncomplicated appendicitis (UAP) remains challenging. The purpose of this study was to construct a safe and economical diagnostic model that can accurately and rapidly differentiate between CAP and UAP. Methods Patient data from 773 appendectomies were retrospectively collected, important features were selected using random forests, and the data were divided into training and test sets in a 3:1 ratio. An integrated learning algorithm, Extreme Gradient Boosting (XGBoost), was introduced to predict the risk of CAP and compared with Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (CART) algorithms. A comprehensive comparison of the four algorithms was performed using model performance metrics such as the area under the receiver’s operating characteristic curve (AUC), sensitivity, specificity, accuracy, precision, negative predictive value(NPV), positive predictive value(PPV),calibration curves, and clinical decision curve analysis (DCA). Result The results show that all four prediction models exhibit some predictive ability. The XGBoost model showed the best prediction with AUC, accuracy, sensitivity, specificity,NPV and PPV of 0.914, 0.855, 0.865, 0.846,0.848 and 0.897, respectively, followed by the SVM model with results of AUC, accuracy, sensitivity, specificity,NPV and PPV of 0.882, 0.819, 0.865, 0.779, 0.770 and 0.871, respectively. XGBoost and SVM models show very good calibration. The XGBoost model showed better net clinical benefit compared to the DCA curves of the other models. Conclusion Predictive models based on the XGBoost algorithm have good performance in predicting the risk of acute appendicitis progressing to complicated appendicitis, which helps to optimize clinical decision making.https://doi.org/10.1186/s12876-025-03847-6Disease risk predictionMachine learningComplicated appendicitisXGBoost modelPredictive modeling |
| spellingShingle | Sunmeng Chen Jianfu Xia Beibei Xu Yi Huang Miaomiao Teng Juyi Pan Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modeling BMC Gastroenterology Disease risk prediction Machine learning Complicated appendicitis XGBoost model Predictive modeling |
| title | Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modeling |
| title_full | Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modeling |
| title_fullStr | Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modeling |
| title_full_unstemmed | Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modeling |
| title_short | Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modeling |
| title_sort | risk prediction and effect evaluation of complicated appendicitis based on xgboost modeling |
| topic | Disease risk prediction Machine learning Complicated appendicitis XGBoost model Predictive modeling |
| url | https://doi.org/10.1186/s12876-025-03847-6 |
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