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The use of electronic cigarette (e-cigarette) is increasing amongadolescents. This is problematic since consuming nicotine at an early age cancause 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 andcigarette that mostly focused on finding and analyzing causes of smoking usingconventional statistics. However, there is a lack of research on developingprediction models, which is more applicable to anti-smoking campaign, aboute-cigarette and cigarette. In this paper, we research the prediction modelsthat can be used to predict an individual e-cigarette user's (includingnon-e-cigarette users) intention to smoke cigarettes, so that one can be earlyinformed about the risk of going down the path of smoking cigarettes. Toconstruct the prediction models, five machine learning (ML) algorithms areexploited and tested for their accuracy in predicting the intention to smokecigarettes among never smokers using data from the 2018 National Youth TobaccoSurvey (NYTS). In our investigation, the Gradient Boosting Classifier, one ofthe prediction models, shows the highest accuracy out of all the other models.Also, with the best prediction model, we made a public website that enablesusers to input information to predict their intentions of smoking cigarettes.

A Study of Machine Learning Models in Predicting the Intention of Adolescents to Smoke Cigarettes