Development and validation of a machine learning model to predict postoperative complications following radical gastrectomy for gastric cancer.

ABSTRACT:

Objective

Postoperative complications significantly adversely affect recovery and prognosis following radical gastrectomy for gastric cancer. We developed and validated machine learning (ML) models to predict these complications and constructed a clinically applicable dynamic nomogram.

Methods

Using a prospectively maintained database, we conducted a retrospective analysis of 1,486 patients from Fujian Cancer Hospital (training cohort) and 498 from the First Hospital of Putian City (validation cohort). Feature selection integrated Lasso regression, the Boruta algorithm, and Recursive Feature Elimination (RFE). Six ML models were developed and evaluated: TreeBagger (TB), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Gaussian Naïve Baye

SUBMITTER: Lin Z 

PROVIDER: S-EPMC12450669 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature