Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modeling

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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Bibliographic Details
Main Authors: Sunmeng Chen, Jianfu Xia, Beibei Xu, Yi Huang, Miaomiao Teng, Juyi Pan
Format: Article
Language:English
Published: BMC 2025-04-01
Series:BMC Gastroenterology
Subjects:
Disease risk prediction
Machine learning
Complicated appendicitis
XGBoost model
Predictive modeling
Online Access:https://doi.org/10.1186/s12876-025-03847-6
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