Machine learning model based on routine blood and biochemical parameters for early diagnosis of diabetic kidney disease | Synapse
March 3, 2026Open Access
Machine learning model based on routine blood and biochemical parameters for early diagnosis of diabetic kidney disease
Key Points
Early diabetic kidney disease is effectively identified by a machine learning model using routine blood parameters, including key predictors.
The model highlights the TyG index and HbA1c as significant factors for predicting diabetic kidney disease.
Machine learning techniques applied to biochemical parameters can enhance early diagnosis of chronic conditions like kidney disease.
The findings support the potential of cost-effective screening options in clinical settings to aid early intervention.
Abstract
The machine learning model successfully identified early DKD using routine parameters, with TyG index, HbA1c, and globulin as key predictors, demonstrating potential as a cost-effective screening tool.