A Decision Tree Approach for Predicting Smokers' Quit Intentions

This paper presents a decision tree approach for predicting smokers' quit intentions using the data from the International Tobacco Control Four Country Survey. Three rule-based classification models are generated from three data sets using attributes in relation to demographics, warning labels, and smokers' beliefs. Both demographic attributes and warning label attributes are important in predicting smokers' quit intentions. The model's ability to predict smokers' quit intentions is enhanced, if the attributes regarding smokers' internal motivation and beliefs about quitting are included.