SHAP-explained machine-learning model for high-risk gastric cancer identification

This externally validated and interpretable short-term GC risk model incorporating endoscopically ascertained AG/IM could provide a practical approach for informing risk-adapted screening workflows. The model could help identify individuals at a higher predicted risk for prospective evaluation and closer clinical review. In addition, SHAP clarifies the main contributors to each prediction by highlighting factors most strongly associated with a higher predicted risk.