Predicting the Stone-Free Status of Percutaneous Nephrolithotomy With the Machine Learning System: Comparative Analysis With Guy's Stone Score and the S.T.O.N.E Score System.

ABSTRACT: Purpose: The aim of the study was to use machine learning methods (MLMs) to predict the stone-free status after percutaneous nephrolithotomy (PCNL). We compared the performance of this system with Guy's stone score and the S.T.O.N.E score system. Materials and Methods: Data from 222 patients (90 females, 41%) who underwent PCNL at our center were used. Twenty-six parameters, including individual variables, renal and stone factors, and surgical factors were used as input data for MLMs. We evaluated the efficacy of four different techniques: Lasso-logistic (LL), random forest (RF), support vector machine (SVM), and Naive Bayes. The model performance was evaluated using the area under the curve (AUC) and compared with that of Guy's stone score and the S.T.O.N.E score system.

SUBMITTER: Zhao H 

PROVIDER: S-EPMC9114350 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature