Comparing data mining methods with logistic regression in childhood obesity prediction

Authors:
Shaoyan Zhang;Christos Tjortjis;Xiaojun Zeng;Hong Qiao;Iain Buchan;John Keane
Affiliations:
School of Computer Science, University of Manchester, Manchester, UK M60 1QD;Department Engineering Informatics and Telecommunications, University of Western Macedonia, Kozani, Greece 50100 and Department of Computer Science, University of Ioannina, Ioannina, Greece 45110;School of Computer Science, University of Manchester, Manchester, UK M60 1QD;School of Computer Science, University of Manchester, Manchester, UK M60 1QD;School of Medicine, University of Manchester, Manchester, UK M13 9PT;School of Computer Science, University of Manchester, Manchester, UK M60 1QD
Venue:
Information Systems Frontiers
Year:
2009

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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.