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

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.
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Anju Verma, Maurizio Fiasché, Maria Cuzzola, Francesco Morabito, Giuseppe Irrera. Knowledge Discovery and Risk Prediction for Chronic Diseases: An Integrated Approach. Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.270-279, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_31〉. 〈hal-01571367〉

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