Background: Despite the promising prospects of utilizing artificial intelligence and machine learning (ML) for comprehensive disease analysis, few models constructed have been applied in clinical practice due to their complexity and the lack of reasonable explanations. In contrast to previous studies with small sample sizes and limited model interpretability, we developed a transparent eXtreme Gradient Boosting (XGBoost)-based model supported by multi-center data, using patients' basic information and clinical indicators to forecast the occurrence of anastomotic leakage (AL) after rectal cancer resection surgery. The model demonstrated robust predictive performance and identified clinically relevant thresholds, which may assist physicians in optimizing perioperative management.

Aim: To develop an interpretable ML model for accurately predicting the occurrence probability of AL after rectal cancer resection and define our clinical alert values for serum calcium ions.

Methods: Patients who underwent anterior resection of the rectum for rectal carcinoma at the Department of Digestive Surgery, Xijing Hospital of Digestive Diseases, Air Force Medical University, and Shaanxi Provincial People's Hospital, were retrospectively collected from January 2011 to December 2021,. Ten ML models were integrated to analyze the data and develop the predictive models. Receiver operating characteristic (ROC) curves, calibration curve, decision curve analysis, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were used to evaluate model performance. We employed the SHapley Additive exPlanations (SHAP) algorithm to explain the feature importance of the optimal model.

Results: A total of ten features were integrated to construct the predictive model and identify the optimal model. XGBoost was considered the best-performing model with an area under the ROC curve (AUC) of 0.984 (95%confidence interval: 0.972-0.996) in the test set (accuracy: 0.925; sensitivity: 0.92; specificity: 0.927). Furthermore, the model achieved an AUC of 0.703 in external validation. The interpretable SHAP algorithm revealed that the serum calcium ion level was the crucial factor influencing the predictions of the model.

Conclusion: A superior predictive model, leveraging clinical data, has been crafted by employing the most effective XGBoost from a selection of ten algorithms. This model, by predicting the occurrence of AL in patients after rectal cancer resection, has identified the significant role of serum calcium ion levels, providing guidance for clinical practice. The integration of SHAP provides a clear interpretation of the model's predictions.

Keywords: Anastomotic leakage; Machine learning; Rectal cancer; SHapley Additive exPlanations algorithms.

©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.

背景： 尽管利用人工智能和机器学习（ML）进行全面疾病分析具有广阔前景，但由于模型复杂性和缺乏合理解释等原因，很少有模型在临床实践中得到应用。与以往样本量小且模型可解释性有限的研究不同，我们开发了一个基于多中心数据的透明eXtreme Gradient Boosting (XGBoost) 模型，使用患者的基线信息和临床指标预测直肠癌切除术后吻合口漏（AL）的发生率。该模型展示了强大的预测性能，并识别了临床相关的阈值，这可能有助于医生优化围手术期管理。

目的： 开发一个可解释的ML模型，准确预测直肠癌切除术后AL发生概率，并定义我们的临床预警值（血清钙离子）。

方法： 我们回顾性收集了2011年1月至2021年12月期间在中国人民解放军空军军医大学西京消化病医院消化外科和陕西省人民医院接受直肠癌前切除术的患者数据。整合十个机器学习模型来分析这些数据并开发预测模型。采用受试者工作特征曲线（ROC）、校准曲线、决策曲线分析、准确率、灵敏度、特异度、阳性预测值、阴性预测值和F1分数评估模型性能。我们使用SHapley Additive exPlanations (SHAP) 算法来解释最优模型的特征重要性。

结果： 总共整合了十个特征构建预测模型并识别出最佳模型。XGBoost被认为是表现最好的模型，在测试集中的ROC曲线下面积（AUC）为0.984（95%置信区间：0.972-0.996），准确率为0.925，灵敏度为0.92，特异度为0.927。此外，在外部验证中该模型的AUC达到0.703。可解释性SHAP算法显示血清钙离子水平是影响模型预测的关键因素。

结论： 通过从十个算法中选择最有效的XGBoost，我们开发了一个基于临床数据的强大预测模型。该模型通过对直肠癌切除术后AL发生的预测，识别了血清钙离子水平的重要作用，为临床实践提供了指导。SHAP的整合为模型的预测提供了一种清晰的解释。

关键词： 吻合口漏；机器学习；直肠癌；SHapley Additive exPlanations 算法。