Identifying Tobacco Control Policy Drivers: A Neural Network Approach

Abstract

This paper presents a neural network approach to investigating Australian smokers' quit motivations that affect their quit attempts. Based on the data from the International Tobacco Control Four Country Survey, Neural network (NN) models are developed to identify smokers' quitting motivations as smokers' quit motivations are significant factors in predicting smokers' quit attempts. In order to identify the underlying tobacco control policies from these quitting motivations, principle component analysis is used to group individual attributes into 4 tobacco control policy drivers: Personal Concerns, Price, Social Restrictions and Social Encouragement which are related to specific tobacco contr..