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Comparing data mining methods with logistic regression in childhood obesity prediction

Author

Listed:
  • Shaoyan Zhang

    (University of Manchester)

  • Christos Tjortjis

    (University of Western Macedonia
    University of Ioannina)

  • Xiaojun Zeng

    (University of Manchester)

  • Hong Qiao

    (University of Manchester)

  • Iain Buchan

    (University of Manchester)

  • John Keane

    (University of Manchester)

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.

Suggested Citation

  • Shaoyan Zhang & Christos Tjortjis & Xiaojun Zeng & Hong Qiao & Iain Buchan & John Keane, 2009. "Comparing data mining methods with logistic regression in childhood obesity prediction," Information Systems Frontiers, Springer, vol. 11(4), pages 449-460, September.
    • Handle: RePEc:spr:infosf:v:11:y:2009:i:4:d:10.1007_s10796-009-9157-0
    DOI: 10.1007/s10796-009-9157-0
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    References listed on IDEAS

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    1. Kweku-Muata Osei-Bryson & Kendall Giles, 2006. "Splitting methods for decision tree induction: An exploration of the relative performance of two entropy-based families," Information Systems Frontiers, Springer, vol. 8(3), pages 195-209, July.
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    Cited by:

    1. Nida Shahid & Tim Rappon & Whitney Berta, 2019. "Applications of artificial neural networks in health care organizational decision-making: A scoping review," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-22, February.
    2. Carlos Magno Sousa & Ewaldo Santana & Marcus Vinicius Lopes & Guilherme Lima & Luana Azoubel & Érika Carneiro & Allan Kardec Barros & Nilviane Pires, 2019. "Development of a Computational Model to Predict Excess Body Fat in Adolescents through Low Cost Variables," IJERPH, MDPI, vol. 16(16), pages 1-12, August.
    3. Davide Barbieri & Nitesh Chawla & Luciana Zaccagni & Tonći Grgurinović & Jelena Šarac & Miran Čoklo & Saša Missoni, 2020. "Predicting Cardiovascular Risk in Athletes: Resampling Improves Classification Performance," IJERPH, MDPI, vol. 17(21), pages 1-9, October.
    4. Cheong Kim & Francis Joseph Costello & Kun Chang Lee & Yuan Li & Chenyao Li, 2019. "Predicting Factors Affecting Adolescent Obesity Using General Bayesian Network and What-If Analysis," IJERPH, MDPI, vol. 16(23), pages 1-18, November.

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