Knowledge Discovery and Risk Prediction for Chronic Diseases: An Integrated Approach

  • Anju Verma
  • Maurizio Fiasché
  • Maria Cuzzola
  • Francesco C. Morabito
  • Giuseppe Irrera
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 363)


A novel ontology based type 2 diabetes 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.


Knowledge discovery knowledge representation chronic disease ontology personalized risk evaluation system 


  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)MATHGoogle 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.
    The FIELD Study Investigators. The need for a large-scale trial of fibrate therapy in diabetes: the rationale and design of the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study. ISRCTN64783481. Cardiovascular Diabetology 3, 9 pages (2004)Google Scholar
  8. 8.
    New Zealand Guidelines Group. Management of diabetes. New Zealand Guidelines Group, Wellington (2003(a),
  9. 9.
    Brown, J.B., Palmer, A.J., et al.: The Mt. Hood challenge: cross-testing two diabetes simulation models. Diabetes Research and Clinical Practice 50(3), S57–S64 (2000a)CrossRefGoogle Scholar
  10. 10.
    Brown, J.B., Russell, A., et al.: The global diabetes model: user friendly version 3.0. Diabetes Research and Clinical Practice 50(3), S15–S46 (2000b)CrossRefGoogle Scholar
  11. 11.
    Lindstrom, J., Tuomilehto, J.: The diabetes risk score. A practical tool to predict type-2 diabetes risk. Diabetes Care 26(3), 725–731 (2003)CrossRefGoogle Scholar
  12. 12.
    Eddy, D.M., Schlessinger, L.: Archimedes. A trial-validated model of diabetes. Diabetes Care 26(11), 3093–3101 (2003a)CrossRefGoogle Scholar
  13. 13.
    Eddy, D.M., Schlessinger, L.: Validation of the Archimedes diabetes model. Diabetes Care 26(11), 3102–3110 (2003b)CrossRefGoogle Scholar
  14. 14.
    Al-Lawati, J.A., Tuomilehto, J.: Diabetes risk score in Oman: A tool to identify prevalent type-2 diabetes among Arabs of the Middle East. Diabetes Research and Clinical Practice 77, 438–444 (2007)CrossRefGoogle Scholar
  15. 15.
    Cornelis, M., Qi, L., et al.: Joint effects of common genetic variants on the risk of type-2 diabetes in U. S. men and women of European ancestry. Annals of Internal Medicine 150, 541–550 (2009)Google Scholar
  16. 16.
    Stern, M., Williams, K., et al.: Validation of prediction of diabetes by the Archimedes Model and comparison with other predictiong models. Diabetes Care 31(8), 1670–1671 (2008)CrossRefGoogle Scholar
  17. 17.
    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)MATHCrossRefGoogle Scholar
  18. 18.
    Vapnik, V.N.: Statistical Learning Theory. Wiley Inter-Science, Chichester (1998)MATHGoogle Scholar
  19. 19.
    Mitchell, M.T., Keller, R., et al.: Explanation-based generalization: A unified view. Machine Learning 1(1), 47–80 (1997)Google Scholar
  20. 20.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)MathSciNetMATHCrossRefGoogle Scholar
  21. 21.
    Zadeh, L.A.: Fuzzy logic. IEEE Computer 21, 83–93 (1988)Google Scholar
  22. 22.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics 15, 116–132 (1985)MATHGoogle Scholar
  23. 23.
    Fischer, J., Koch, L., et al.: Inactivation of the Fto gene protects from obesity. Nature 458, 894–899 (2009)CrossRefGoogle Scholar
  24. 24.
    Verma, A.: An Integrated Approach for Ontology Based Personalized Modeling: Chronic Disease Ontology, Risk Evaluation and Knowledge Discovery. LAP LAMBERT Academic Publishing (2010)Google Scholar
  25. 25.
    Kasabov, N., Song, Q., Benuskova, L., Gottgtroy, P., Jain, V., Verma, A., Havukkala, I., Rush, E., Pears, R., Tjahjana, A., Hu, Y., MacDonel, S.: Integrating Local and Personalised Modelling with Global Ontology Knowledge Bases for Biomedical and Bioinformatics Decision Support. In: Smolin, et al. (eds.) Computational Intelligence in Bioinformatics, ch. 4, Springer, Heidelberg (2008)Google Scholar
  26. 26.
    Kasabov, N., Hu, Y.: Integrated optimisation method for personalised modelling and case study applications. Int. Journal of Functional Informatics and Personalised Medicine 3(3), 236–256 (2010)CrossRefGoogle Scholar
  27. 27.
    Fiasché, M., Verma, A., Cuzzola, M., Iacopino, P., Kasabov, N., Morabito, F.C.: Discovering Diagnostic Gene Targets and Early Diagnosis of Acute GVHD Using Methods of Computational Intelligence over Gene Expression Data. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009, Part II. LNCS, vol. 5769, pp. 10–19. Springer, Heidelberg (2009) ISBN/ISSN: 978-3-642-04276-8CrossRefGoogle Scholar
  28. 28.
    Fiasché, M., Cuzzola, M., Fedele, R., Iacopino, P., Morabito, F.C.: Machine Learning and Personalized Modeling Based Gene Selection for Acute GvHD Gene Expression Data Analysis. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part I. LNCS, vol. 6352, pp. 217–223. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  29. 29.
    Fiasché, M., Cuzzola, M., Irrera, G., Iacopino, P., Morabito, F.C.: Advances in Medical Decision Support Systems for Diagnosis of Acute Graft-versus-Host Disease: Molecular and Computational Intelligence Joint Approaches. Frontiers in Biology, doi: 10.1007/s11515-011-1124-8Google Scholar
  30. 30.
    Fiasché, M., Cuzzola, M., Iacopino, P., Kasabov, N., Morabito, F.C.: Personalized Modeling based Gene Selection for acute GvHD Gene Expression Data Analysis: a Computational Framework Proposed. Australian Journal of Intelligent Information Processing Systems 12(4) (2010); Machine Learning Applications (Part II)Google Scholar

Copyright information

© International Federation for Information Processing 2011

Authors and Affiliations

  • Anju Verma
    • 1
  • Maurizio Fiasché
    • 1
    • 2
  • Maria Cuzzola
    • 1
  • Francesco C. Morabito
    • 2
  • Giuseppe Irrera
    • 1
  1. 1.CTMO - Transplant Regional Center of Stem Cells and Cellular Therapy, ”A. Neri”Hospital “Morelli” of Reggio CalabriaItaly
  2. 2.DIMETUniversity “Mediterranea” of Reggio CalabriaItaly

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