Paper Info
Title
Ontology Based Personalized Modeling for Type 2 Diabetes Risk Analysis: 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.
Year
DOI
Venue
2009
10.1007/978-3-642-10684-2_40
ICONIP
Keywords
Field
DocType
chronic disease,expert system,decision support system,ontology-based personalized risk evaluation,integrated approach,ontological representation,personalized modeling,transductive neuro-fuzzy inference system,personalized risk,diabetes risk analysis,diabetes risk analysis system,global knowledge representation,knowledge representation,risk analysis
Transduction (machine learning),Ontology-based data integration,Ontology,Disease Ontology,Knowledge representation and reasoning,Computer science,Risk analysis (business),Expert system,Decision support system,Artificial intelligence,Machine learning
Conference
Volume
ISSN
Citations 
5864
0302-9743
5
PageRank 
References 
Authors
0.49
4
6
Name
Order
Citations
PageRank
Anju Verma1192.70
Maurizio Fiasché2499.23
Maria Cuzzola3194.12
Pasquale Iacopino4142.51
Francesco C. Morabito5175.46
Nikola K Kasabov63645290.73