A nomograph model for predicting the risk of diabetes nephropathy.

Objective: Using machine learning to construct a prediction model for the risk of diabetes kidney disease (DKD) in the American diabetes population and evaluate its effect.

Methods: First, a dataset of five cycles from 2009 to 2018 was obtained from the National Health and Nutrition Examination Survey (NHANES) database, weighted and then standardized (with the study population in the United States), and the data were processed and randomly grouped using R software. Next, variable selection for DKD patients was conducted using Lasso regression, two-way stepwise iterative regression, and random forest methods. A nomogram model was constructed for the risk prediction of DKD. Finally, the predictive performance, predictive value, calibration, and clinical effectiveness of the model were evaluated through the receipt of ROC curves, Brier score values, calibration curves (CC), and decision curves (DCA). In addition, we will visualize it.

Results: A total of 4371 participants were selected and included in this study. Patients were randomly divided into a training set (n = 3066 people) and a validation set (n = 1305 people) in a 7:3 ratio. Using machine learning algorithms and drawing Venn diagrams, five variables significantly correlated with DKD risk were identified, namely Age, Hba1c, ALB, Scr, and TP. The area under the ROC curve (AUC) of the training set evaluation index for this model is 0.735, the net benefit rate of DCA is 2%-90%, and the Brier score is 0.172. The area under the ROC curve of the validation set (AUC) is 0.717, and the DCA curve shows a good net benefit rate. The Brier score is 0.177, and the calibration curve results of the validation set and training set are almost consistent.

Conclusion: The DKD risk nomogram model constructed in this study has good predictive performance, which helps to evaluate the risk of DKD as early as possible in clinical practice and formulate relevant intervention and treatment measures. The visual result can be used by doctors or individuals to estimate the probability of DKD risk, as a reference to help make better treatment decisions.