Advertisement

Bayesian Classification of Personal Histories - An application to the Obesity Epidemic

  • Christopher R. StephensEmail author
  • José Antonio Borras Gutiérrez
  • Hugo Flores
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)

Abstract

Bayesian classifiers are an important tool in machine learning. The Naive Bayes classifier in particular has shown itself to be a robust, computationally efficient and transparent approximation. However, due to its strong assumption of feature independence it is often too easily discarded. Understanding under what circumstances feature correlations are likely to occur and how to diagnose them is an important first step in improving the Naive Bayes approximation. Furthermore, combining features can lead to enhanced approximations. In this paper we show the benefits of a Generalised Bayes Approximation that accounts for feature correlations while, at the same time, showing that an important area where feature correlations are ubiquitous is in time series that represent aspects of human behaviour. Using data that represent historical patterns of exercise we show how more predictive and more insightful models for obesity can be constructed using a Generalised Bayes approximation that combines historical features, thereby capturing the idea of human habits. In particular, by analysing feature combinations we show that abandoning good past exercise habits is more correlated with obesity than never having had them in the first place.

Keywords

Naive Bayes approximation Generalised Bayes Time series History Obesity Exercise 

Notes

Acknowledgements

This work was supported by CONACyT Fronteras grant 1093.

References

  1. Domingos, P., Pazzani, M.: Beyond independence: conditions for the optimality of the simple Bayesian Classifier. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 105–112. Morgan Kaufmann (1996)Google Scholar
  2. Stephens, C.R., Huerta, H.F., Linares, A.R.: Machine Learning, vol. 107, Issue 2, pp. 397–441 (2017)Google Scholar
  3. Teixeira, P.J., Carraca, E.V., Marques, M.M., et al.: Successful behavior change in obesity interventions in adults: a systematic review of self-regulation mediators. BMC Med. 13, 84 (2015).  https://doi.org/10.1186/s12916-015-0323-6CrossRefGoogle Scholar
  4. World Health Organization: Obesity: preventing and managing the global epidemic (No. 894). World Health Organization (2000)Google Scholar
  5. Church, T.S., Thomas, D.M., et al.: Trends over 5 decades in US occupation-related physical activity and their associations with obesity. PloS one 6(5), e19657 (2011)CrossRefGoogle Scholar
  6. Kononenko, I.: Semi-naive Bayesian classifier. In: Proceedings of the Sixth European Working Session on Learning, pp. 206–219. Springer, Berlin (1991)Google Scholar
  7. World Health Organisation: Global recommendations on physical activity for health (2018). http://www.who.int/dietphysicalactivity/factsheet_recommendations/en/
  8. Holland, J.: Adaptation in Natural and Artificial Systems, 2nd edn. MIT Press, Cambridge (1992)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Christopher R. Stephens
    • 1
    Email author
  • José Antonio Borras Gutiérrez
    • 2
  • Hugo Flores
    • 3
    • 4
  1. 1.C3 - Centro de Ciencias de la Complejidad, Instituto de Ciencias NuclearesUniversidad Nacional Autónoma de MéxicoMexico CityMexico
  2. 2.Facultad de CienciasUniversidad Nacional Autónoma de MéxicoMexico CityMexico
  3. 3.TechMileageScottsdaleUSA
  4. 4.C3 - Centro de Ciencias de la ComplejidadUniversidad Nacional Autonoma de MexicoMexico CityMexico

Personalised recommendations