BACKGROUND

Considering the increasing prevalence of adolescent smoking in recent years, this study proposes a machine learning (ML) approach for distinguishing adolescents who are prone to start smoking and those who do not directly confess to smoking.

METHODS

We used two repeated measures cross-sectional studies, including data from 7940 individuals as distinct training and test datasets. Utilizing the randomized least absolute shrinkage and selector operator (LASSO), the most influential factors were selected. We then investigated the performance of different ML approaches for the automatic classification of students into smoker/nonsmoker and low-risk/high-risk categories.

RESULTS

Randomized LASSO feature selection prioritized 15 factors, including peer influence, risky behaviors, attitude and school policy toward smoking, family factors, depression, and sex as the most influential factors in smoking. Applying different ML approaches to the three study plans yielded an AUC of up to 0.92, sensitivity of up to 0.88, PPV of up to 0.72, specificity of up to 0.98, and NPV of up to 0.99.

CONCLUSIONS

The results showed the capability of our ML approach to distinguish between classes of smokers and nonsmokers. This model can be used as a brief screening tool for automated prediction of individuals susceptible to smoking for more precise preventive intervention plans focusing on adolescents.