Use machine learning to predict pulmonary metastasis of esophageal cancer: a population-based study.

ABSTRACT:

Background

This study aims to establish a predictive model for assessing the risk of esophageal cancer lung metastasis using machine learning techniques.

Methods

Data on esophageal cancer patients from 2010 to 2020 were extracted from the surveillance, epidemiology, and end results (SEER) database. Through univariate and multivariate logistic regression analyses, eight indicators related to the risk of lung metastasis were selected. These indicators were incorporated into six machine learning classifiers to develop corresponding predictive models. The performance of these models was evaluated and compared using metrics such as The area under curve (AUC), accuracy, sensitivity, specificity, and F1 score.

Results

A total of 20,249 confirmed cases of esophageal cancer we

SUBMITTER: Fang Y 

PROVIDER: S-EPMC11405433 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature