  The use of electronic cigarette (e-cigarette) is increasing among
adolescents. This is problematic since consuming nicotine at an early age can
cause harmful effects in developing teenager's brain and health. Additionally,
the use of e-cigarette has a possibility of leading to the use of cigarettes,
which is more severe. There were many researches about e-cigarette and
cigarette that mostly focused on finding and analyzing causes of smoking using
conventional statistics. However, there is a lack of research on developing
prediction models, which is more applicable to anti-smoking campaign, about
e-cigarette and cigarette. In this paper, we research the prediction models
that can be used to predict an individual e-cigarette user's (including
non-e-cigarette users) intention to smoke cigarettes, so that one can be early
informed about the risk of going down the path of smoking cigarettes. To
construct the prediction models, five machine learning (ML) algorithms are
exploited and tested for their accuracy in predicting the intention to smoke
cigarettes among never smokers using data from the 2018 National Youth Tobacco
Survey (NYTS). In our investigation, the Gradient Boosting Classifier, one of
the prediction models, shows the highest accuracy out of all the other models.
Also, with the best prediction model, we made a public website that enables
users to input information to predict their intentions of smoking cigarettes.
