抄録
Our aim is to develop high accurate Extracorporeal Shock Wave Lithotripsy (ESWL) outcome prediction by introducing complex machine learning models. To obtain interpretation of these complex models, factor importance evaluation by SHapley Additive exPlanations (SHAP) value was performed. In this retrospective study, the data was collected from 214 subjects, where single session ESWL outcome was defined by the residue of stone fragments smaller than 4 mm. Outcome prediction by three machine learning algorithms was performed, and accuracy verification and factor evaluation by SHAP were executed. As a result, we obtained superior accuracy for the two types of ensemble tree models compared to the single decision tree, and clear model interpretations by SHAP. These results may encourage the use of complex models with interpretability difficulties and allow more accurate ESWL outcome prediction.
