Advertisement

Ontology Based Personalized Modeling for Chronic Disease Risk Analysis: An Integrated Approach

  • Anju Verma
  • Nikola Kasabov
  • Elaine Rush
  • Qun Song
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5506)

Abstract

A novel ontology based chronic disease risk analysis system framework is described, which allows the creation of global knowledge representation (ontology) and personalized modeling for a decision support system. A computerized model focusing on organizing knowledge related to three chronic diseases and genes has been developed in an ontological representation that is able to identify interrelationships for the ontology-based personalized risk evaluation for chronic diseases. The personalized modeling is a process of model creation for a single person, based on their personal data and the information available in the ontology. A transductive neuro-fuzzy inference system with weighted data normalization is used to evaluate personalized risk for chronic disease. This approach aims to provide support for further discovery through the integration of the ontological representation to build an expert system in order to pinpoint genes of interest and relevant diet components.

Keywords

Fuzzy Rule Personalized Risk Personalized Modeling Zealand Medical Journal Ontological Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kasabov, N.: Global, local and personalized modeling and profile discovery in Bioinformatics: An integrated approach. Pattern Recognition Letters 28(6), 673–685 (2007)CrossRefGoogle Scholar
  2. 2.
    Gruber, T.R.: A translation approach to portable ontologies. Knowledge Acquisition 5, 199–220 (1993)CrossRefGoogle Scholar
  3. 3.
    Fensel, D.: Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce, 2nd edn. Springer, Heidelberg (2004)CrossRefzbMATHGoogle Scholar
  4. 4.
    Chandrasekaran, B., Josephson, J.R., Benjamins, V.R.: What are ontologies, and why do we need them? Intelligent Systems and Their Applications 14, 20–26 (1999)CrossRefGoogle Scholar
  5. 5.
    Owens, A.: Semantic Storage: Overview and Assessment. Technical Report IRP Report 2005, Electronics and Computer Science, U of Southampton (2005)Google Scholar
  6. 6.
    Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American (May 17) (2001)Google Scholar
  7. 7.
    Anderson, K.M., Odell, P.M., Wilson, P.W.F., Kannel, W.B.: Cardiovascular disease risk profiles. American Heart J. 121(1), 293–298 (1991)CrossRefGoogle Scholar
  8. 8.
    Jackson, R.: Updated New Zealand cardiovascular disease risk-benefit prediction guide. British Medical Journal 320, 709–710 (2000)CrossRefGoogle Scholar
  9. 9.
    Milne, R., Gamble, G., Whitlock, G., Jackson, R.: Framingham Heart study risk equation predicts first cardiovascular event rates in New Zealanders at the population level. The New Zealand Medical Journal 116(1185) (2003)Google Scholar
  10. 10.
    Bannink, L., Wells, S., Broad, J., Riddell, T., Jackson, R.: Web-based assessment of cardiovascular disease risk in routine primary care practice in New Zealand: the first 18,000 patients (PREDICT CVD-1). The New Zealand Medical Journal, 119(1245) (2006)Google Scholar
  11. 11.
    Song, Q., Kasabov, N.: TWNFI - a transductive neuro-fuzzy inference system with weighted data normalization for personalized modeling. Neural Networks 19(10), 1591–1596 (2006)CrossRefzbMATHGoogle Scholar
  12. 12.
    Alberti, K.G.M.M., Zimmet, P., Shaw, J.: Metabolic syndrome-a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabetic Medicine 23, 469–480 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Anju Verma
    • 1
  • Nikola Kasabov
    • 1
  • Elaine Rush
    • 1
  • Qun Song
    • 1
  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

Personalised recommendations