Fig. 2: Development and validation of the nomogram model. | Nutrition & Diabetes

Fig. 2: Development and validation of the nomogram model.

From: Interpretable machine learning-derived nomogram model for early detection of diabetic retinopathy in type 2 diabetes mellitus: a widely targeted metabolomics study

Fig. 2

Developed nomogram for diabetic retinopathy (A), and the ROC curve and decision curves analysis curve of the Nomogram model, Rhee et al. model, Aspelund et al. model, Hippisley-Cox and Coupland model, and Dagliati et al. model in the training set (B, C) and testing set (D, E). Notes: nomogram model, thiamine triphosphate, systolic blood pressure, duration of diabetes; Rhee et al. model, glutamine/glutamate ratio; Aspelund et al. model, gender, systolic blood pressure, duration of diabetes and glycated hemoglobin; Hippisley-Cox and Coupland model, age, BMI, systolic blood pressure, cholesterol/high-density lipoprotein ratio, glycated hemoglobin; Dagliati et al. model, age, gender, duration of diabetes, BMI, glycated hemoglobin, hypertension, smoke; none, net benefit when all patients are considered as not having the outcome (diabetic retinopathy); all, net benefits when all patients are considered as having the outcome. The preferred model is the model with the highest net benefit at any given threshold. Abbreviations: MEDN430 thiamine triphosphate, sBp systolic blood pressure, DM_duration duration of diabetes.

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