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

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

Published: 2026 Jan

Authors

, , , , , ,

Abstract

Moderately increased (micro) albuminuria serves as a critical early indicator of Diabetic Kidney Disease (DKD). However, traditional screening methods that rely on laboratory-based analyses face significant challenges in enabling timely and continuous monitoring. This study addresses these limitations by introducing a non-invasive approach for albuminuria risk detection, allowing real-time estimation of mild albuminuria increases using vital signs and body measurements.

Keywords: Albuminuria, DKD, Diabetic kidney disease, Estimated cardiac output (eCO), Machine learning, Non-invasive medical screening

Source

Computer methods and programs in biomedicine

Publication Type

Journal Article

Language

English

PubMed ID

41138643

MeSH terms

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