Abstract
This study aimed to develop and evaluate a machine learning based risk prediction model for intraoperative hypothermia (IOH) in patients undergoing thoracoscopic lung cancer surgery and interpret the model using the SHapley Additive exPlanations (SHAP) method to assess the contribution of specific features to the prediction results. A retrospective analysis was conducted on 717 patients who underwent thoracoscopic lung cancer surgery at a tertiary hospital in Wuhan from January 2022 to December 2023. The dataset was randomly divided into a training set (n = 502) and a testing set (n = 215) at a 7:3 ratio. A random forest (RF) algorithm was used to build the prediction model. Model performance was assessed using accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve. The Brier score of the calibration curve was used to evaluate model fit, and decision curve analysis (DCA) was used to assess clinical utility. The SHAP method was applied to interpret the importance and influence of each predictive feature. The area under the receiver operating characteristic curve of the random forest-based prediction model in the testing set was 0.753, the F1 score was 0.80, the recall rate was 0.87, the accuracy rate was 0.732, the precision rate was 0.74, 95% CI (0.69-0.82), the sensitivity was 0.789, the specificity was 0.614, and the Brier score was 0.196. Decision curve analysis results confirmed the model's good clinical practicability. The SHAP diagram visually displayed that intraoperative infusion volume, surgery duration, age, anesthesia duration, body mass index, and hemoglobin were the 6 most important features influencing IOH risk, and there were also interaction effects between features. The SHAP method enhanced the interpretability of the machine learning model, identifying key risk factors for IOH in thoracoscopic lung cancer surgery. This approach can assist medical staff in screening high-risk factors and developing personalized hypothermia prevention programs for lung cancer patients.