Development and validation of an interpretable machine learning model for predicting urinary incontinence at 6 months after robot-assisted radical prostatectomy.

Urinary incontinence adversely affects quality of life as a common complication after robot-assisted radical prostatectomy (RARP). To address this, our study sought to build and explain a machine learning (ML) model for predicting 6-month post-RARP incontinence risk. This single-center retrospective analysis enrolled 852 patients with localized prostate cancer (PCa) who underwent RARP from January 2021 to December 2024. Models were developed employing seven ML algorithms. Their performance was primarily evaluated through Receiver Operating Characteristic (ROC) curves and Decision Curve Analysis (DCA). Subsequently, the optimal model was interpreted using SHAP (SHapley Additive exPlanations) analysis. The final model incorporated seven key predictors: prostate volume, membranous urethral length (MUL), age, body mass index (BMI), transurethral resection of the prostate (TURP), T stage, and basal surgical margin status. In the testing set, the Light Gradient Boosting Machine (LightGBM) model demonstrated the best performance, achieving an AUC of 0.784 and an accuracy of 0.793. Through SHAP analysis, intuitive visualizations were generated to illustrate how each feature contributed to individual risk predictions. This ML-based prediction model accurately estimates the risk of urinary incontinence at 6 months after RARP and demonstrates favorable clinical utility. This tool provides a dependable means for timely detection and risk classification of patients at elevated risk, potentially enabling the adoption of tailored preventive measures.