Predicting Biochemical Recurrence After Robot-Assisted Prostatectomy with Interpretable Machine Learning Model.

ABSTRACT: Background: This study aimed to develop and evaluate machine learning (ML) models to predict biochemical recurrence (BCR) after robot-assisted radical prostatectomy (RARP). Methods: We retrospectively analyzed clinical data from 1125 patients who underwent RARP between July 2013 and December 2023. The dataset was divided into a training set (70%) and a testing set (30%) using a stratified sampling strategy. Five ML models were developed using the training set. Model performance was evaluated on the testing set using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 scores. Additionally, model interpretability was assessed using SHapley Additive exPlanations (SHAP) values to determine the contribution of individual fea

SUBMITTER: Zhang T 

PROVIDER: S-EPMC12524934 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature