An Ontology-based Decision Support System for Obesity Tracking During Childhood and Adolescence

Pediatric obesity patients are required to be continuously tracked by the health experts for providing more efficient treatment. This thesis discusses the design, implementation, and potential use of an ontology based mobile obesity management and consultation system which is a smart consultation and tracking system for children and adolescent patients during their treatments. The system does not only share instant gathered medical data to a physician but also examines the data as a smart medical assistant. The system includes an ontology-based inference engine, is a decision support module, and provides suggestions as supportive treatment steps to gain personalized appropriate dietary habits and physical activities. The applied methodologies and main technical contributions are discussed as three aspects: (1) Obesity Tracking Ontology, (2) Semantic Web Rule Knowledge base and (3) Inference Engine. In this thesis, unlike the other similar studies, ontology and rule based smart medical assistant which have different functionalities from adults’ obesity management is considered especially for obesity management of children and adolescents. The system also includes intensive pediatric health care expert involvement. The reported results of the applied case studies are promising in demonstrating the applicability, effectiveness and efficiency of the proposed approach. Keywords: Pediatric patient, obesity, pediatric patient obesity tracking, knowledge base, expert system, ontology, SQWRL, SWRL rule.