Comparing data mining methods with logistic regression in childhood obesity prediction

Shaoyan Zhang, Christos Tjortjis, Xiaojun Zeng, Hong Qiao, Iain Buchan, John Keane

    Research output: Contribution to journalArticlepeer-review


    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. © 2009 Springer Science+Business Media, LLC.
    Original languageEnglish
    Pages (from-to)449-460
    Number of pages11
    JournalInformation Systems Frontiers
    Issue number4
    Publication statusPublished - Sept 2009


    • Accuracy
    • Machine learning
    • Medical data mining
    • Prediction
    • Public health


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