Research Article
Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy
Table 1
Baseline characteristics of the study population.
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for continuous variables: the value was calculated by a test. % for categorical variables: the value was calculated by a weighted chi-square test. MAU: urinary microalbumin; Duration of T2D: duration of type 2 diabetes mellitus; PCV: erythrocyte pressure volume; SBP: systolic blood pressure; FBG: fasting blood glucose; eGFR: glomerular filtration rate; IGF-1: insulin sample growth factor-1; INS: insulin; FT3: free triiodothyronine; GLB: globulin; Hb: hemoglobin concentration; CHOL: cholesterol; ALP: alkaline phosphatase; UA: blood uric acid; HbA1c: glycosylated hemoglobin; HDL: high-density lipoprotein; Tyr: chitosan; Ser: serine; AC: abdominal circumference; Met: methionine; Apo AI: apolipoprotein A1; Apo B, apolipoprotein B; TSH: thyroid-stimulating hormone; AGI: alpha-glucosidase inhibitor; GADA: glutamic acid decarboxylase antibody; Cys: cysteine; drink: alcohol consumption or not; Dpp-4: dipeptidyl peptidase-4; TZDs: thiazolidinediones; GLP-1: glucagon-like peptide; SGLT-2: sodium-glucose cotransporter protein 2 inhibitor; C2: acetylcarnitine; C4DC: succinylcarnitine; C5DC: glutarylcarnitine; C24: tetracosanoic carnitine. |