Ontology Based Personalized Modeling for Chronic Disease Risk Analysis: An Integrated Approach
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.
KeywordsFuzzy Rule Personalized Risk Personalized Modeling Zealand Medical Journal Ontological Representation
Unable to display preview. Download preview PDF.
- 5.Owens, A.: Semantic Storage: Overview and Assessment. Technical Report IRP Report 2005, Electronics and Computer Science, U of Southampton (2005)Google Scholar
- 6.Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American (May 17) (2001)Google Scholar
- 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.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