Development of a machine learning-based model for predicting postoperative survival in gastric cancer

BACKGROUND: Accurate prediction of postoperative survival is crucial for the personalized management of gastric cancer. However, the development of robust predictive models is often constrained by incomplete clinical data, while their clinical utility is limited by poor interpretability and the absence of practical applications. AIM: To develop an interpretable machine learning model for predicting 3-year survival following gastric cancer surgery. A novel data imputation method was proposed to handle missing values, and a user-friendly online tool was developed to facilitate clinical decision-making. METHODS: A retrospective analysis was conducted on a group of 304 patients with gastric adenocarcinoma. A hybrid imputation method (HDI-MF-Gower) was developed and compared against conventional techniques. Key prognostic factors were identified by integrating least absolute shrinkage and selection operator regression with the Boruta algorithm. Subsequently, ten machine learning models were trained and validated. RESULTS: The proposed HDI-MF-Gower method demonstrated superior imputation accuracy. Seven features were selected for the final model. The extra trees classifier achieved the best performance on the independent validation set, with an area under the curve of 0.853 and an accuracy of 0.772. The optimal model was interpreted using SHapley Additive exPlanations analysis and deployed as an online prediction tool. CONCLUSION: A robust and interpretable predictive model integrating advanced data imputation was successfully developed. The deployed tool facilitates individualized prognostic assessment and shows potential for enhancing personalized treatment planning in gastric cancer.