Development and validation of a machine learning model for survival risk stratification after esophageal cancer surgery.

ABSTRACT:

Background

Prediction of prognosis for patients with esophageal cancer(EC) is beneficial for their postoperative clinical decision-making. This study's goal was to create a dependable machine learning (ML) model for predicting the prognosis of patients with EC after surgery.

Methods

The files of patients with esophageal squamous cell carcinoma (ESCC) of the thoracic segment from China who received radical surgery for EC were analyzed. The data were separated into training and test sets, and prognostic risk variables were identified in the training set using univariate and multifactor COX regression. Based on the screened features, training and validation of five ML models were carried out through nested cross-validation (nCV). The performance of each model was evaluated usin

SUBMITTER: Xu J 

PROVIDER: S-EPMC9780661 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature