Development and validation of an interpretable shap-based machine learning model for predicting postoperative complications in laryngeal cancer.

ABSTRACT:

Objective

Postoperative complications remain a major concern in laryngeal cancer surgery, often requiring invasive interventions or intensive care. This study aimed to develop and validate an interpretable machine learning (ML) model to preoperatively predict Clavien-Dindo Grade ≥ III complications and support risk-informed perioperative decision-making.

Methods

We conducted a retrospective study using a temporally split cohort of laryngeal cancer patients. Postoperative complications were graded using the Clavien-Dindo (CD) classification. Eight ML algorithms were trained and evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Model interpretability was assessed using SHapley Additive exPlanations (SHAP). A w

SUBMITTER: Li C 

PROVIDER: S-EPMC12512493 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature