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Published in:
2008 | OriginalPaper | Chapter
Performance Evaluation of Intelligent Prediction Models on Smokers’ Quitting Behaviour
Authors : Chang-Joo Yun, Xiaojiang Ding, Susan Bedingfield, Chung-Hsing Yeh, Ron Borland, David Young, Sonja Petrovic-Lazarevic, Ken Coghill, Jian Ying Zhang
Published in: Intelligent Data Engineering and Automated Learning – IDEAL 2008
Publisher: Springer Berlin Heidelberg
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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.