Abstract
This paper evaluates the performance of intelligent models using decision trees, rough sets, and neural networks for predicting smokers’ quitting behaviour. 18 models are developed based on 6 data sets created from the International Tobacco Control Four Country Survey. 13 attributes about smokers’ beliefs about quitting (BQ) and 13 attributes about smokers’ beliefs about smoking (BS) are used as inputs. The output attribute is the smokers’ status of making a quit attempt (MQA) or planning to quit (PTQ). The neural network models outperform both decision tree models and rough set models in terms of prediction ability. Models using both BQ and BS attributes as inputs perform better than models using only BQ or BS attributes. The BS attributes contribute more to MQA, whereas the BQ attributes have more impact on PTQ. Models for predicting PTQ outperform models for predicting MQA. Determinant attributes that affect smokers’ quitting behaviour are identified.
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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), iii12–iii18 (2006)
Ding, X., Bedingfield, S., Yeh, C.-H., Zhang, J., Petrovic-Lazarevic, S., Coghill, K., Borland, R., Young, D.: A Decision Tree Approach for Predicting Smokers’ Quit Intentions. In: The 2008 International Conference on Communications, Circuits and Systems, pp. 1159–1163 (2008)
Hammond, D., Fong, G.T., McNeil, A., Borland., R., Cummings, K.M.: Effectiveness of Cigarette Warning Labels in Informing Smokers about the Risks of Smoking: Findings from the International Tobacco Control (ICT) Four Country Survey. Tobacco Control 15(3), iii19–iii25 (2006)
Harris, F., MacKintosh, A.M., Anderson, S., Hastings, G., Borland, R., Fong, G.T., Hammond, D., Cummings, K.M.: Effects of the 2003 Advertising/Promotion Ban in the United Kingdom on Awareness of Tobacco Marketing: Findings from the International Tobacco Control (ITC) Four Country Survey. Tobacco Control 15(3), iii26–iii33 (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), iii83–iii94 (2006)
Quinlan, J.R.: Generating Production Rules from Decision Trees, http://dli.iiit.ac.in/ijcai/IJCAI-87-VOL1/PDF/063.pdf
Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough Sets. Communications of the ACM 38(11), 89–95 (1995)
Slowinski, R.: Intelligent Decision Support. In: Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, Dordrecht (1992)
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)
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Yun, CJ. et al. (2008). Performance Evaluation of Intelligent Prediction Models on Smokers’ Quitting Behaviour. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_27
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DOI: https://doi.org/10.1007/978-3-540-88906-9_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88905-2
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