Identifying gastric intestinal metaplasia risk based on clinical indicators: a machine learning predictive model based on the SHAP methodology

This study highlights the innovative use of serological biomarkers to assess the risk of GIM. We found that certain markers related to liver and kidney function are strong predictors of GIM development. Additionally, the application of SHAP values improves the understanding of how features contribute to predictions, while the newly developed web-based calculator offers a practical tool for clinicians to evaluate GIM risk more easily.