A Machine Learning Model for Predicting the Risk of Diabetic Nephropathy in Individuals with Type 2 Diabetes Mellitus

Tingting LI^1,2*Jinbo CHEN³Xin ZHANG^1,2Kaiwen WANG¹Xuesen ZHAO¹Yi Cao^1,2Zhen XU⁴Shiyue WANG⁵Peng SU²Xioayan HE³Yang YANG³Dong MA^1,2Xiaohua LIANG³

• ¹Department of Biochemistry and Molecular Biology, Key Laboratory of Neural and Vascular Biology, Hebei Medical University, Shijiazhuang, China
• ²School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, China
• ³Department of General Medicine, Shijiazhuang Second Hospital, Shijiazhuang, China
• ⁴School of Medicine, Hebei University of Engineering, Handan, Hebei Province, China
• ⁵College of Medicine, Zhengzhou University, Zhengzhou, Henan Province, China

We conducted aretrospective collection of clinical data from 10,057 patients diagnosed with T2DM at Shijiazhuang Second Hospital. A random selection of 15% of these patients (n=1,508) was utilized for external validation. The remaining 8,549 patients were divided into a training set (n=5,985) and a validation set (n=2,564) using a simple random sampling method in a 7:3 ratio. Subsequently, we employed LASSO regression to identify variables significantly associated with DKD in T2DM patients. These variables were incorporated into eight distinct predictive models: Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), KNeighbors Classifier (KNN), Gradient Boosting Classifier (GBM), AdaBoost Classifier (AdaBoost), and Extreme Gradient Boosting (XGBoost). The models' predictive performance was assessed using metrics such as the area under the curve (AUC), accuracy, F1 score, and Brier score. Finally, we developed an online calculator to estimate DKD risk in T2DM patients.Fifteen features-namely gender, age, systolic blood pressure (SBP), blood urea nitrogen (BUN), creatinine (Cr), BUN/Cr ratio, uric acid (UA), hemoglobin A1c (HbA1c ) , microalbuminuria, presence of diabetic retinopathy (DR), hypertension, coronary heart disease (CHD), history of cerebral infarction, family history of diabetes, and family history of CHD-associated with DKD were selected using LASSO regression. Among eight evaluated models, the XGBoost algorithm demonstrated superior performance on both training and validation datasets, with an AUCof 0.932 (95%CI: 0.926-0.938) and 0.930, (95%CI: 0.920-0.939), respectively. The model achieved an accuracy of 0.845 and 0.844, sensitivity of 0.834 and 0.850, specificity of 0.857 and 0.837, F1 score of 0.847 and 0.848, and a Brier score of 0.167 and 0.166, respectively. Decision curve analysis (DCA) further validated the superiority of the XGBoost model over other models across a range of clinically relevant risk thresholds, yielding the highest net benefits. Finally, an online predictive calculator for the occurrence of DKD was developed based on the XGBoost model, utilizing a cut-off value of 50.7%.The developed XGBoost model demonstrated optimal predictive accuracy for the occurrence of DKD in patients with T2DM. This model facilitated the construction of an online prediction calculator, offering an accessible and practical tool for both patients and clinicians.