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

purposeThe 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.

methodsPatient 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).

resultThe 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.

conclusionPredictive 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.