Non-invasive prediction of detrusor underactivity in benign prostatic hyperplasia: an interpretable machine learning framework to optimize surgical selection

Objective To develop and internally validate an interpretable, non-invasive machine learning framework to predict detrusor underactivity (DU) in patients with benign prostatic hyperplasia (BPH). Methods This retrospective cohort study enrolled 538 urodynamically evaluated BPH patients. A rigorous multidimensional feature selection pipeline (LASSO, Boruta, and Recursive Feature Elimination) distilled 15 baseline clinical, anatomical, and uroflowmetry parameters into a parsimonious five-feature subset. Five supervised machine learning algorithms were trained and systematically compared. Shapley Additive exPlanations (SHAP) analysis was integrated for global and local interpretability. Results The optimized XGBoost model demonstrated superior discriminatory performance (AUC = 0.958), significantly outperforming traditional multivariable logistic regression (AUC = 0.787). XGBoost consistently exhibited superior calibration and higher net clinical benefit across varied threshold probabilities. Crucially, SHAP global dependence plots revealed non-linear pathological trajectories, notably demonstrating a U-shaped risk profile for bladder wall thickness (BWT) that was not captured by classical linear statistical detection. Local SHAP visualizations effectively translated complex probabilistic outputs into individualized clinical reasoning. Conclusion The interpretable XGBoost framework serves as a robust non-invasive risk stratification tool for DU, decoding complex non-linear clinical interactions. This algorithm holds significant potential to optimize preoperative patient selection and mitigate surgical failures in borderline clinical scenarios. Clinical trial registration Identifier 2026-048.