Web Based Health Recommender System Using Rough Sets, Survival Analysis and Rule-Based Expert Systems

  • Puntip Pattaraintakorn
  • Gregory M. Zaverucha
  • Nick Cercone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4482)


We propose a health recommendation system architecture using rough sets, survival analysis approaches and rule-based expert systems. Our main goal is to recommend clinical examinations for patients or physicians from patients’ self reported data. Such data will be treated as condition attributes, while survival time from a follow-up study will be treated as the target function. We have amalgamated rough set theory, relational databases, statistics, soft computing and several pertinent techniques to generate a hybrid intelligent system for survival analysis. This study represents the completion of our system by adding a recommendation module.


Rough sets Survival analysis Recommender system 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blatter, M., Zhang, Y., Maslow, S.: Exploring an Opinion Network for Taste Prediction: An Empirical Study. Physica A 373, 753–758 (2007)CrossRefGoogle Scholar
  2. 2.
    Liu, D., Shih, Y.: Hybrid Approaches to Product Recommendation Based on Customer Lifetime Value and Purchase Preferences. J. Syst. Software 77, 181–191 (2005)CrossRefGoogle Scholar
  3. 3.
    Elisa, L.T., John, W.W.: Statistical Methods for Survival Data Analysis, 3rd edn. John Wiley & Sons, Chichester (2003)zbMATHGoogle Scholar
  4. 4.
    Bazan, J., et al.: Searching for the Complex Decision Reducts: The Case Study of the Survival Analysis. In: Zhong, N., et al. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 160–168. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Zaluski, J., et al.: Rough Set Theory and Decision Rules in Data Analysis of Breast Cancer Patients. In: Peters, J.F., et al. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Song, X., et al.: Assessment of Individual Risk of Death Using Self-report Data: An Artificial Neural Network Compared to a Frailty Index. J. Am. Geriatr. Soc. 52, 1180–1184 (2004)CrossRefGoogle Scholar
  7. 7.
    Pattaraintakorn, P., Cercone, N., Naruedomkul, K.: Hybrid Intelligent Systems: Selecting Attributes for Soft-Computing Analysis. In: Proc. 29th IEEE Computer Society International Computer Software and Applications Conference (COMPSAC 2005), pp. 319–325. IEEE Press, Los Alamitos (2005)Google Scholar
  8. 8.
    Pattaraintakorn, P., Cercone, N., Naruedomkul, K.: Selecting Attributes for Soft-computing Analysis in Hybrid Intelligent Systems. In: Ślęzak, D., et al. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 698–708. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Pattaraintakorn, P., Cercone, N., Naruedomkul, K.: Rule Analysis with Rough Sets Theor. In: Proc. 2006 IEEE International Conference on Granular Computing (GrC 2006), pp. 582–585. IEEE Press, Los Alamitos (2006)CrossRefGoogle Scholar
  10. 10.
    Pattaraintakorn, P., Cercone, N.: Hybrid Rough Sets-Population Based System. In: Peters, J.F., et al. (eds.) Transactions on Rough Sets VII. LNCS, vol. 4400, pp. 190–205. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Pattaraintakorn, P., et al.: A Foundation of Rough Sets Theoretical and Computational Hybrid Smart System for Survival Analysis. Artif. Intell. Med. (under submission)Google Scholar
  12. 12.
    Pawlak, Z.: Rough sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)zbMATHGoogle Scholar
  13. 13.
    Coppin, B.: Artificial Intelligence Illuminated. Jones and Bartlett Publishers, Inc., Sudbury (2004)Google Scholar
  14. 14.
    An, A., Cercone, N.: ELEM2: A Learning System for More Accurate Classifications. In: Mercer, R.E. (ed.) Canadian AI 1998. LNCS, vol. 1418, pp. 426–441. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  15. 15.
    Sweeney, L.: k-Anonymity: A Model for Protecting Privacy. Int. J. Uncertain. Fuzz. 10(5), 557–570 (2002)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Puntip Pattaraintakorn
    • 1
  • Gregory M. Zaverucha
    • 2
  • Nick Cercone
    • 3
  1. 1.Department of Mathematics and Computer Science, Faculty of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, 10520Thailand
  2. 2.School of Computer Science, University of Waterloo, Ontario, N2L 3G1Canada
  3. 3.Faculty of Science and Engineering, York University, Ontario, M3J 1P3Canada

Personalised recommendations