Machine learning-based prediction of 5-year survival in elderly NSCLC patients using oxidative stress markers.

ABSTRACT:

Background

Oxidative stress plays a significant role in aging and cancer, yet there is currently a lack of research utilizing machine learning models to examine the relationship between oxidative stress and prognosis in elderly non-small cell lung cancer (NSCLC) patients.

Methods

This study included elderly NSCLC patients who underwent radical lung cancer resection from January 2012 to April 2018, exploring the relationship between Oxidative Stress Score (OSS) and prognosis. Machine learning techniques, including Decision Trees (DT), Random Forest (RF), and Support Vector Machine (SVM), were employed to develop predictive models for 5-year overall survival (OS).

Results

The datasets consisted of 1647 patients in the training set, 705 in the internal validation set, an

SUBMITTER: Chen H 

PROVIDER: S-EPMC11540553 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature