This document discusses the use of machine learning algorithms in predicting the recurrence of renal stones using clinical data. The study compares different algorithmic techniques, including support vector machine (SVM), random forest (RF), and logistic regression (GLM), to find the best model for predicting urolithiasis recurrence. The data set used in the study consisted of 401 clinical cases, and the RF model demonstrated the best performance in terms of internal validity. The study concludes that the RF model has better generalization ability compared to the other models. The authors declare no conflict of interests. [Extracted from the article]