Discovering Semantic Relationships between NCD and Lifestyle Patterns using Ontologies

Abstract

The volume of biomedical spatial information available on line increases day by day, which need to be exploited and shared by users in different knowledge areas. NCD 1 kill 38 million people each year and considering the seriousness of the problem globally, there are already underway government strategies to reduce risk factors and early detection and timely treatment. In this article, it's discussed the problem of the NCD using Semantic Web tools. The proposed system uses six ontologies which formalize concepts related to people, physical activity, the NCD, nutrition, geographic regions and symptoms to give information about lifestyle patterns. SWRL 2 rules are used to define accurate axioms which allow improving classification of the individuals.

Ontology-Based Reasoning for Educational Assistance in Noncommunicable Chronic Diseases

“…In addition to these, the proposed ontology reused the ontologies defined by Aguilar et al [9] and Skillen et al [76] as both define context concepts. In relation to NCDs, other ontologies were also analyzed and considered including the ontologies OntoMex proposed by Somodevilla et al [65] and Alian et al [10], and DO by Schriml et al [77].…”

“…Somodevilla et al [65] proposed a system that integrates six ontologies (person, physical activity, NCDs, nutritional information, geographic regions, and chronic disease symptoms). Semantic rules were created to answer four competency questions: (1) What are the main risk factors for having diabetes?…”

“…The reuse included the classes "Context", "Activity", "Time", "Location", and "Device". In representing the concepts of NCDs, the ontologies OntoMex proposed by Somodevilla et al [65] and Alian et al [10], and the Disease Ontology (DO) by Schriml et al [77], were used. In addition, the DOID ontology was selected because it represents disease terms correlated with the domain, and SNOMED CT that consists of a collection of medical vocabularies aiming at supporting the clinical data.…”