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 control policies. To examine the impact of these tobacco control policy drivers on smokers’ making a quit attempt, a set of NN models using 4 policy drivers are also built. Experimental results indicate that in comparison with cigarette price and social restrictions, educating smokers of health benefits from quitting and social encouragement for cessation of smoking have more impacts in encouraging smokers to make a quit attempt.
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References
Fong, G.T., Cummings, K.M., Borland, R., Hastings, G.B., Hyland, P., Giovino, G.A., Hammond, D., Thompson, M.E.: The conceptual framework of the International Tobacco Control (ITC) Policy Evaluation Project. Tobacco Control 15(3), 3–11 (2005)
Thompson, M.E., Fong, G.T., Hammond, D., Boudreau, C., Driezen, P., Hyland, P., Borland, R., Cummings, K.M., Hastings, G.B., Siahpush, M., Machintosh, A.M., Laux, F.L.: Methods of the International Tobacco Control (ITC) Four Country Survey. Tobacco Control 15(3), 12–18 (2006)
Hyland, A., Borland, R., Li, Q., Yong, H.-H., McNeill, A., Fong, G.T., O’Connor, R.J., Cummings, K.M.: Individual-Level Predictors of Cessation Behaviours among Participants in the International Tobacco Control (ITC) Four Country Survey. Tobacco Control 15(3), 83–94 (2006)
Yun, C.J., Ding, X., Bedingfield, S., Yeh, C.-H., Borland, R., Young, D., Petrovic-Lazarevic, S., Coghill, K., Zhang, J.Y.: Performance Evaluation of Intelligent Prediction Models on Smokers’ Quitting Behaviour. In: Fyfe, C., Kim, D., Lee, S.-Y., Yin, H. (eds.) IDEAL 2008. LNCS, vol. 5326, pp. 210–216. Springer, Heidelberg (2008)
Yong, H.-H., Borland, R., Siahpush, M.: Quitting-Related Beliefs, Intentions, and Motivations of Older Smokers in Four Countries: Findings from the International Tobacco Control Policy Evaluation Survey. Addictive Behaviors 30, 777–788 (2005)
Zhou, X., Nonnemaker, J., Sherrill, B., Gilsenan, A.W., Coste, F., West, R.: Attempts to quit smoking and relapse: Factors associated with success or failure from the ATTEMPT cohort study. Addictive Behaviors 34, 365–373 (2009)
Yeh, C.-H., Lin, Y.-C.: Neural Network Models for Transforming Consumer Perception into Product Form Design. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 799–804. Springer, Heidelberg (2006)
Ding, X., Bedingfield, S., Yeh, C.-H., Borland, R., Young, D., Zhang, J.Y., Petrovic-Lazarevic, S., Coghill, K.: A Decision Tree Approach for Predicting Smokers’ Quit Intentions. Journal of Electronic Science and Technology 6(3), 284–288 (2008)
Smith, K.A.: Introduction to Neural Networks and Data Mining for Business Applications. Eruditions Publishing, Melbourne (1999)
Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986)
Ding, X., Bedingfield, S., Yeh, C.-H., Borland, R., Young, D., Petrovic-Lazarevic, S., Coghill, K., Zhang, J.Y.: A rule based approach to modeling the effect of tobacco control policies. In: Proceedings of the 4th International Conference on Information and Automation for Sustainability (ICIAfS 2008), Colombo, Sri Lanka, pp. 496–501 (2008)
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Ding, X. et al. (2009). Identifying Tobacco Control Policy Drivers: A Neural Network Approach. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_86
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DOI: https://doi.org/10.1007/978-3-642-10684-2_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10682-8
Online ISBN: 978-3-642-10684-2
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