Fairness of machine learning readmission predictions following open ventral hernia repair.

ABSTRACT:

Introduction

Few models have predicted readmission following open ventral hernia repair (VHR), and none have assessed fairness. Fairness evaluation assesses whether predictive performance is similar across demographic groups, ensuring that biases are not propagated. Therefore, we generated an interpretable machine learning model to predict readmission following open VHR while assessing fairness.

Methods

NSQIP (2018-2021) was queried for open VHR. We developed an XGBoost model to predict unplanned readmissions within 30 days of surgery with fivefold cross-validation. Performance and fairness were assessed by demographic groups: gender (female vs. male), ethnicity (Hispanic vs. non-Hispanic), and race (non-White vs. White). We identified influential features within demographic

SUBMITTER: Zander T 

PROVIDER: S-EPMC12345327 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature