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)

Abstract

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.

Keywords

Knowledge discovery knowledge representation chronic disease ontology personalized risk evaluation system 

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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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