The anonymized data and analysis scripts for DM prediction in GC

Distant metastasis (DM) of gastric cancer (GC) represents a significant health challenge due to its high mortality rates, necessitating advancements in early detection and management strategies. The objective of this study was to create a machine learning (ML) model that is interpretable for preoperative prediction of DM in GC.We retrospectively analyzed 1,009 GC patients, of which 769 were from Zhejiang Cancer Hospital as development cohort and 240 from Zhejiang Provincial Hospital of Traditional Chinese Medicine as external test cohort. Nine clinical features， and four composite indices derived from ten laboratory indicators were selected as candidate features. The dataset was balanced using the borderline Synthetic Minority Over-sampling Technique (SMOTE) and the Edited Nearest Neighbors (ENN) under-sampling method. Univariate and multivariate analyses were used to identified key metastasis-related features. Based on the identified features, we developed predictive models incorporating five ML algorithms, with performance evaluated via receive operating characteristic (ROC) curves, recall, precision-recall (PR) curves. Ultimately, Shapley additive explanations (SHAP) analysis were applied to rank the feature importance and explain the final model.