Survival After Radical Cystectomy for Bladder Cancer: Development of a Fair Machine Learning Model.

ABSTRACT:

Background

Prediction models based on machine learning (ML) methods are being increasingly developed and adopted in health care. However, these models may be prone to bias and considered unfair if they demonstrate variable performance in population subgroups. An unfair model is of particular concern in bladder cancer, where disparities have been identified in sex and racial subgroups.

Objective

This study aims (1) to develop a ML model to predict survival after radical cystectomy for bladder cancer and evaluate for potential model bias in sex and racial subgroups; and (2) to compare algorithm unfairness mitigation techniques to improve model fairness.

Methods

We trained and compared various ML classification algorithms to predict 5-year survival after radical cystecto

SUBMITTER: Carbunaru S 

PROVIDER: S-EPMC11694706 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature