Enhancing explainability of random survival forests in predicting stent patency risk for malignant colonic obstruction.

ABSTRACT:

Background

This study aims to enhance the explainability and predictive accuracy of the Random Survival Forest (RSF) algorithm in predicting stent patency risk for patients with malignant colonic obstruction.

Methods

The RSF algorithm was applied to clinical prognostic data of 109 patients with malignant colonic obstruction who underwent self-expandable metallic stent (SEMS) procedures between September 2014 and October 2023. We combined the RSF variable importance and Least Absolute Shrinkage and Selection Operator (Lasso) regression to identify the final predictive variables. And the performance of the RSF model was compared with the Cox Proportional Hazards (CPH) model using both global and local explanation methods.

Results

The RSF model demonstrated superior pred

SUBMITTER: Wan Y 

PROVIDER: S-EPMC12551288 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature