Predictive value of machine learning model based on CT values for urinary tract infection stones.

ABSTRACT: Preoperative diagnosis of infection stones presents a significant clinical challenge. We developed a machine learning model to predict urinary infection stones using computed tomography (CT) values, enabling in vivo preoperative identification. In this study, we included 1209 patients who underwent urinary lithotripsy at our hospital. Seven machine learning algorithms along with eleven preoperative variables were used to construct the prediction model. Subsequently, model performance was evaluated by calculating AUC and AUPR for subjects in the validation set. On the validation set, all seven machine learning models demonstrated strong discrimination (AUC: 0.687-0.947). Additionally, the XGBoost model was identified as the optimal model significantly outperforming the traditional LR

SUBMITTER: Li J 

PROVIDER: S-EPMC11616073 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature