Leveraging Machine Learning to Predict Quit Attempts Among Adult Cigarette Smokers.

Abstract

Introduction: Cigarette smoking is the leading preventable cause of premature death in the US. Quitting can significantly reduce the risk of adverse health effects. However, much is unknown about the factors prospectively associated with the behavioral step of making a quit attempt. The present study leveraged supervised machine learning, data from a prior experimental study, and exploratory methods to investigate longitudinal associations between smoking history, cognitive and behavioral indices, and quit attempts reported at a four-week follow-up assessment.

Aims and methods: Secondary analysis of a project that enrolled 278 adult cigarette smokers (18-69 years old) for a prospective study of predictors of making a quit attempt(s). Analyses were based on N = 231 with non-missing data through the one-month follow-up, separated into training (n = 185, of whom 36 made a quit attempt) and testing (n =46, of whom 9 made a quit attempt) subsets.

Results: In the testing subset, a random forest model with class weights to address class imbalance and a linear support vector machine model performed best of the models tested, albeit not very well (eg, F₁ values of 0.36 in the testing set). Feature importance metrics suggested that contemplation status and perceived risks of "I will eat more" and "I will miss the taste of cigarettes" were of highest importance in classifications. All models had better specificity than sensitivity. Poor performance in the testing set may have resulted in part from small sample size.

Conclusion: While reliable predictions of quit attempts were not obtained across the train-test split, the present study leverages bottom-up, data-driven approaches, and machine learning methods to explore the understudied behavioral step of initiating a quit attempt. Future studies should continue exploring predictors of quit attempts, along with how they may differ from predictors of sustained smoking cessation. Data-driven machine learning methods have the potential to progress precision medicine efforts by clarifying generalizable mechanisms underlying clinical outcomes.