An explainable machine learning model for predicting bladder tumor aecurrence risk

Key Points

• Accurate prediction of bladder tumor recurrence risk is achieved using a seven-feature XGBoost model, enhancing clinical utility.
• The model reveals transparent feature contributions, making it easier for clinicians to understand risk factors early.
• Assessment highlights the importance of risk stratification, indicating targeted surveillance strategies could be developed faster.
• This model may empower better clinical strategies in managing patients, enabling tailored approaches for bladder cancer follow-ups.