Personalized prediction for recurrence of cystitis glandularis: insights from SHAP and machine learning models.

ABSTRACT:

Background

Cystitis glandularis (CG) is a rare urological condition characterized by glandular metaplasia of the bladder mucosa. Recurrence following transurethral resection (TUR) is a significant clinical challenge. Traditional predictive models often fail to capture the complexity of the data, resulting in insufficient accuracy. In contrast, machine learning (ML) has demonstrated substantial potential in medical prediction by identifying and analyzing complex patterns that are undetectable by conventional methods. This study aims to develop and evaluate an interpretable ML model to predict recurrence after TUR for CG, thereby improving clinical decision-making and patient outcomes.

Methods

We analyzed predictors of recurrence using the least absolute shrinkage and selectio

SUBMITTER: Yuan Y 

PROVIDER: S-EPMC11986474 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature