Investigation of Stratified Sampling in ESWL Outcome Prediction Models

Abstract

Ureteral stone disease refers to mineral mass in the ureter, which extends from the kidney to the bladder. One of the least invasive and widely used treatments of ureteral stone is extracorporeal shock wave lithotripsy (ESWL). However, ESWL has a success rate of approximately 70%. Therefore, it is important to predict the treatment outcomes based on preoperative images and clinical findings. Although prior studies have identified several features effective in predicting ESWL outcomes (success or failure), these studies have not sufficiently addressed the impact of data bias and class imbalance on prediction accuracy. In this study, we perform stratified sampling on the features that were validated in previous studies to predict ESWL and assess its impact on prediction accuracy. We utilized CT/X-ray images and clinical findings from 162 patients who chose ESWL as their initial treatment. We predicted treatment outcomes by machine learning and compared the accuracy of predictions made with and without stratified sampling. Stratified sampling based on stone size achieved the highest AUC and accuracy of 0.850 and 0.897, respectively. These findings confirm that incorporating stratified sampling with effective features enhances the accuracy of ESWL outcome prediction models.