Identifying Key Predictors of Smoking Cessation Success: Text-Based Feature Selection Using a Large Language Model.

ABSTRACT:

Background

The most effective way to reduce mortality and morbidity among current smokers is to quit smoking. Although about half of smokers attempted to quit, only one-tenth succeeded in 2022.

Objective

To identify key predictors of smoking cessation success to inform cessation interventions and increase quitting rates.

Methods

We analyzed data from waves 5 and 6 of the Population Assessment of Tobacco and Health (PATH) study (December 2018 to November 2021). Using OpenAI's GPT-4.1, we identified the top 45 variables from wave 5 that are highly predictive of 12-month smoking abstinence in wave 6, based on descriptions of survey variables. We then validated the predictive power of the GPT-4.1-selected variables by comparing the performance of eXtreme Gradient Boosting

SUBMITTER: Le TTT 

PROVIDER: S-EPMC12204296 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature