Refine XGBoost with SHAP explainability for non-invasive early detection of diabetic kidney disease: Estimated cardiac output as a potential indicator

This study demonstrates that a fine-tuned, non-invasive XGBoost model using simple clinical measures can support albuminuria monitoring and early DKD detection without laboratory tests. While the selected predictors may not represent the definitive or optimal feature set, their strong performance highlights the potential of leveraging easily obtainable, clinically relevant measures. In particular, the contribution of eCO underscores a promising direction for exploring heart-kidney-metabolism interactions in DKD risk assessment. Together, these findings highlight a scalable, non-invasive tool for resource-limited settings, an interpretable framework for clinical trust, and a pathway to refining feature sets for both accuracy and biological insight.