Notably, the RF and xGB machine learning model demonstrated the highest AUC (AUC = 0.972, as depicted in Fig. 4B), indicating their superior performance in diagnosing Diabetic Kidney Disease (DKD).In summary, xGB machine learning model performed the best model in terms of root mean square of residuals and diagnostic performance compared with other three models [Fig. 4A-C].The five most important genes DUSP1,ZFP36,PDK4,CD44 and RGS4 from xGB model were selected as hub DE-FRGs.A nomogram for diagnosing DKD was constructed based on the five genes [Fig. 4D]. This evidence concerns the gene CD44 and diabetic kidney disease.