NDLI: Comparing data mining methods with logistic regression in childhood obesity prediction

Content Provider	Springer Nature Link
Author	Zhang, Shaoyan Tjortjis, Christos Zeng, Xiaojun Qiao, Hong Buchan, Iain Keane, John
Copyright Year	2009
Abstract	The epidemiological question of concern here is “can young children at risk of obesity be identified from their early growth records?” Pilot work using logistic regression to predict overweight and obese children demonstrated relatively limited success. Hence we investigate the incorporation of non-linear interactions to help improve accuracy of prediction; by comparing the result of logistic regression with those of six mature data mining techniques.The contributions of this paper are as follows: a) a comparison of logistic regression with six data mining techniques: specifically, for the prediction of overweight and obese children at 3 years using data recorded at birth, 6 weeks, 8 months and 2 years respectively; b) improved accuracy of prediction: prediction at 8 months accuracy is improved very slightly, in this case by using neural networks, whereas for prediction at 2 years obtained accuracy is improved by over 10%, in this case by using Bayesian methods. It has also been shown that incorporation of non-linear interactions could be important in epidemiological prediction, and that data mining techniques are becoming sufficiently well established to offer the medical research community a valid alternative to logistic regression.
Starting Page	449
Ending Page	460
Page Count	12
File Format	PDF
ISSN	13873326
Journal	Information Systems Frontiers
Volume Number	11
Issue Number	4
e-ISSN	15729419
Language	English
Publisher	Springer US
Publisher Date	2009-02-24
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Medical data mining Machine learning Public health Prediction Accuracy Operations Research/Decision Theory Systems Theory, Control Management of Computing and Information Systems Business Information Systems
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Networks and Communications Information Systems Software

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