Background: Postoperative pulmonary complications (PPCs) are common and have a negative impact on postoperative morbidity and mortality, with associated medical resource use and cost care plan. Management of preoperative and intraoperative risk factors has been shown to reduce the occurrence of PPCs. Therefore, this study aimed to develop a risk prediction model for PPCs based on explainable machine learning (ML) methods and evaluate its predictive performance in order to enhance the prevention and intervention for PPCs.

Methods: In this study, the medical records of 1,629 patients who underwent thoracoscopic surgery were collected from two clinical groups at the Affiliated Hospital of Guangdong Medical University between August 2018 and October 2021. Five categories of data were used as predictors, including patient demographics, medical history and comorbidities, laboratory studies, intraoperative vital signs, and surgical procedure-related data. Seven ML methods, including random forest (RF), adaptive boosting (AdaBoost), extra trees (ET), extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), and two ensemble learning methods, including voting classifier (Voting), and stacking-logistic regression (Stacking-LR), were used to predict the occurrence of PPCs in patients undergoing thoracoscopic surgery. The model performance was validated in internal, temporal, and external phases. Additionally, an explainable approach based on ML methods and the SHapely Additive exPlanation (SHAP) algorithm was used for calculating the PPCs risk and generating individual explanations of the model …