Analysis of breast feeding data using data mining methods

ABSTRACT

The purpose of this study is to demonstrate the benefit of using common data mining techniques on survey data where statistical analysis is routinely applied. The statistical survey is commonly used to collect quantitative information about an item in a population. Statistical analysis is usually carried out on survey data to test hypothesis. We report in this paper an application of data mining methodologies to breast feeding survey data which have been conducted and analysed by statisticians. The purpose of the research is to study the factors leading to deciding whether or not to breast feed a new born baby. Various data mining methods are applied to the data. Feature or variable selection is conducted to select the most discriminative and least redundant features using an information theory based method and a statistical approach. Decision tree and regression approaches are tested on classification tasks using features selected. Risk pattern mining method is also applied to identify groups with high risk of not breast feeding. The success of data mining in this study suggests that using data mining approaches will be applicable to other similar survey data. The data mining methods, which enable a search for hypotheses, may be used as a complementary survey data analysis tool to traditional statistical analysis.

References

1. Chen, J., He, H., Li, J., Jin, H., McAullay, D., Williams, G., Sparks, R. & Kelman, C. (2005), Representing association classification rules mined from health data, in Proceedings of 9th International Conference on Knowledge-Based & Intelligent Information & Engineering Systems (KES2005), Melbourne, Australia, pp. 1225--1231.
2. Cover, T. M. & Thomas., J. A. (1991), Elements of Information Theory, Wiley-Interscience.
3. Fleiss, J. L. (1981), Statistical Methods for Rates and Proportions, Wiley.
4. Fleuret, F. (2004), 'Fast binary feature selection with conditional mutual information', Journal of Machine Learning Research 5, 1531--1555.
5. Gu, L., Li, J., He, H., Williams, G., Hawkins, S. & Kelman, C. (2003), Association rule discovery with unbalanced class, in Proceedings of the 16th Australian Joint Conference on Artificial Intelligence (AI03), Lecture Notes in Artificial Intelligence, Perth, Western Australia, pp. 221--232.
6. He, H., Jin, H. & Chen, J. (2005), Automatic feature selection for classification of health data, in Proceedings of The 18th Australian Joint Conference on Artificial Intelligence (AI2005), Sydney, Australia, pp. 910--913.
7. Hegney, D., Fallon, T., O'Brien, M., Plank, A., Doolan, J., Brodribb, W., Hennessy, J., Laurent, K. & Baker, S. (2003), The Toowoomba Infant Feeding Support Service Project: Report on Phase 1 A Longitudinal Needs Analysis of Breastfeeding Behaviours and Supports in the Toowoomba Region.
8. Jin, H., Chen, J., Kelman, C., He, H., McAullay, D. & O'Keefe, C. M. (2006), Mining unexpected associations for signalling potential adverse drug reactions from administrative health databases, in PAKDD'06, pp. 867--876.
9. Jin, H.-D., Shum, W., Leung, K.-S. & Wong, M.-L. (2004), 'Expanding self-organizing map for data visualization and cluster analysis', Information Sciences 163, 157--173.
10. Jin, H., Wong, M.-L. & Leung, K.-S. (2005), 'Scalable model-based clustering for large databases based on data summarization', IEEE Transactions on Pattern Analysis and Machine Intelligence 27(11), 1710--1719.
11. Kohavi, R. & John, G. (1997), 'Wrappers for feature selection', Artificial Intelligence pp. 273--324.
12. Kramer, M. S. & Kakuma, R. (2003), Optimal duration of exclusive breastfeeding, The Cochrane Library.
13. Li, J., Fu, A. W.-C., He, H., Chen, J., Jin, H., McAullay, D., Williams, G., Sparks, R. & Kelman, C. (2005), Mining risk patterns in medical data, in Proceedings of KDD'05, pp. 770--775.
14. McAullay, D., Williams, G., Chen, J., Jin, H., He, H., Sparks, R. & Kelman, C. (2005), A delivery framework for health data mining and analytics, in V. Estivill-Castro, ed., Twenty-Eighth Australasian Computer Science Conference (ACSC2005), Vol. 38 of CRPIT, ACS, Newcastle, Australia, pp. 381--390.
15. Quinlan, J. (1993), C4.5: Programs for Machine Learning, Morgan Kaufmann.
16. Riodan, J. M. (1997), 'Commentary. the cost of not breastfeeding: a commentary.', Journal of Human Lactation 13(2), 93--97.
17. Shannon., C. E. (1948), 'A mathematical theory of communication', Bell System Technical Journal 27, 379--423, 623--656.
18. Smith, J. (2001), Mothers milk, money and markets, Ann Congress Perinatal Society Australia and New Zealand.
19. Smith, J. P., Thompson, J. F. & Ellwood, D. A. (2002), 'Hospital system costs of artificial infant feeding: Estimates for the australian capital territory', Australian and New Zealand Journal of Public Health 26(6), 543--551.
20. Wang, G., Lochovsky, F. H. & Yang, Q. (2004), Feature selection with conditional mutual information maxmin in text categorization, in Proceedings of CIKM'04, Washington, US, pp. 8--13.
21. WHO (2001), The optimal duration of exclusive breastfeeding, World Health Organization.
22. Yang, Y. & Pedersen, J. O. (1997), A comparative study on feature selection in text categorization, in Proceedings of International Conference on Machine Learning, Nashville, TN, USA.
23. Yu, L. & Liu, H. (2004), Redundancy based feature selection for microarray data, in Proceedings of KDD'04, ACM Press, New York, NY, USA, pp. 737--742.

Index Terms

• Applied computing

  • Life and medical sciences

• Computing methodologies

  • Machine learning

    • Machine learning approaches

• Information systems

  • Information systems applications

    • Data mining

• Mathematics of computing

  • Probability and statistics