Machine learning models for predicting renal injury in patients with gout.

BACKGROUND: Renal injury is a severe complication among individuals diagnosed with gout. This research constructed a machine learning predictive model to assess renal injury risk in gout patients. METHODS: In this study, we trained predictive models for renal injury in patients with gout using NHANES, from 2007 to 2018 database. Extreme Gradient Boosting (XGBoost), support vector machine (SVM) and K-Nearest Neighbors (KNN) were used to train models. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), along with calibration curves and standard evaluation metrics including sensitivity, specificity, accuracy, and F1 score. RESULTS: A cohort of 1,203 patients was analyzed using seventeen variables to develop the predictive model. Extreme Gradient Boosting (XGBoost) was found to be the most effective model due to the area under the receiver operating characteristic curve (AUC). Extreme Gradient Boosting (XGBoost) was explained using variable importance. The four most important variables are blood urea nitrogen, age, uric acid, and urinary albumin. CONCLUSIONS: This research successfully developed machine learning (ML) models to predict renal impairment in gout patients, with the XGBoost model demonstrating superior performance among the three models tested. And we constructed a Web-based tool for calculating the probability of kidney injury in gout patients based on the model XGBoost. We developed a web-based tool that leverages the XGBoost model to estimate the likelihood of renal injury in patients with gout.