Prognosis and risk factor assessment of patients with advanced lung cancer with low socioeconomic status: model development and validation.

ABSTRACT:

Background

Lung cancer, a major global health concern, disproportionately impacts low socioeconomic status (SES) patients, who face suboptimal care and reduced survival. This study aimed to evaluate the prognostic performance of traditional Cox proportional hazards (CoxPH) regression and machine learning models, specifically Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), in patients with advanced lung cancer with low SES.

Design

A retrospective study.

Method

The 949 patients with advanced lung cancer with low SES who entered the hospice ward of a tertiary hospital in Wuhan, China, from January 2012 to December 2021 were randomized into training and testing groups in a 3:1 ratio. CoxPH regression methods a

SUBMITTER: Cui J 

PROVIDER: S-EPMC11389553 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature