Background: Chronic kidney disease (CKD) is a causal relationship with hypertension. Renal hypertension, the main complication of CKD, is a traditional risk factor for cardiovascular events.
Objective: This retrospective study aimed to establish a risk prediction model for CKD with renal hypertension (RH) using machine learning algorithms.
Methods: Using the electronic medical record database of seven large hospitals in Chongqing, 1572 patients with CKD were selected. Based on the presence of RH, they were divided into RH (n = 400) and non-RH (n = 1172) groups. Data from 70% of patients were randomly allocated to the training set to construct the prediction model. The remaining 30% was used as the test set for internal verification. Single-factor logistic regression and correlation analysis were used to screen input indicators. Prediction models were constructed using these machine learning algorithms: support vector machine, random forest, XGBoost, LightGBM, GBDT, and CatBoost. The optimal parameters of these algorithms were determined using the grid search algorithm. The predictive values of the models constructed for predicting CKD with RH were compared.
Results: Urinary protein, urinary occult blood, creatinine, cystatin C, age, creatine kinase-MB, and β2 microglobulin were predictors of CKD with RH. The XGBoost model performed best with a sensitivity of 0.820, a specificity of 0.945, an F1 score of 0.840, and an area under the relative operating characteristic curve of 0.935.
Conclusion: The clinical prediction model constructed by the XGBoost algorithm had the potential to predict CKD with RH.