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
Affiliations:
Clayton School of Information Technology, Monash University, Victoria, Australia;Clayton School of Information Technology, Monash University, Victoria, Australia;Clayton School of Information Technology, Monash University, Victoria, Australia;Clayton School of Information Technology, Monash University, Victoria, Australia;VicHealth Centre for Tobacco Control, the Cancer Council Victoria, Australia;VicHealth Centre for Tobacco Control, the Cancer Council Victoria, Australia;Department of Management, Monash University, Victoria, Australia;Department of Management, Monash University, Victoria, Australia;Department of Management, Monash University, Victoria, Australia
Venue:
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Year:
2008

Citing 2
Cited 1

Rough sets

Communications of the ACM
Neural network models for transforming consumer perception into product form design

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III

Identifying Tobacco Control Policy Drivers: A Neural Network Approach

ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II

Quantified Score

Hi-index	0.00

Visualization

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.