ObjectiveTo develop a machine learning model to identify preoperative, intraoperative, and postoperative high-risk factors of laparoscopic inguinal hernia repair (LHR) and to predict recurrent hernia. Methods The patients after LHR from 2010 to 2018 were included. Twenty-nine characteristic variables were collected, including patient demographic characteristics, chronic medical history, laboratory test characteristics, surgical information, and postoperative status of the patients. Four machine learning algorithms, including extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), were used to construct the model. We also applied Shapley additive explanation (SHAP) for visual interpretation of the model and evaluated the model using the k-fold cross-validation method, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). ResultsA total of 1 178 patients with inguinal hernias were included in the study, including 114 patients with recurrent hernias. The XGBoost algorithm showed the best performance among the four prediction models. The ROC curve results showed that the area under the curve (AUC) value of XGBoost was 0.985 in the training set and 0.917 in the validation set, which showed high prediction accuracy. The K-fold cross-validation method, calibration curve, and DCA curve showed that the XGBoost model was stable and clinically useful. The AUC value in the independent validation set was 0.86, indicating that the XGBoost prediction model has good extrapolation. The results of SHAP analysis showed that mesh size, mesh fixtion, diabetes, hypoproteinemia, obesity, smoking history, low intraoperative percutaneous arterial oxygen saturation (SpO2), and low intraoperative body temperature were strongly associated with recurrent hernia. ConclusionThe predictive model of recurrent hernia after LHR in patients derived from the XGBoost machine learning algorithm in this study can assist clinicians in clinical decision making.