Research on key indicators for diagnosis and prediction of rheumatoid arthritis based on GBDT+LR embedded feature selection model

The proposed multidimensional embedded feature selection framework showed strong diagnostic performance and interpretability in identifying key biomarkers for RA, effectively addressing the issue of indicator redundancy and enhancing diagnostic precision. This pragmatic application of an established GBDT+LR framework, integrated with SHAP for interpretability and built on routine clinical data, offers potential clinical utility in RA diagnosis.