Ontology based personalized modeling for chronic disease risk analysis: an integrated approach

Authors:
Anju Verma;Nikola Kasabov;Elaine Rush;Qun Song
Affiliations:
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland;Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland;Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland;Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

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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.