title: An Integrated Children Disease Prediction Tool within a Special Social Network.
abstract: This paper proposes a social network with an integrated children disease prediction system developed by the use of the specially designed Children General Disease Ontology (CGDO). This ontology consists of children diseases and their relationship with symptoms and Semantic Web Rule Language (SWRL rules) that are specially designed for predicting diseases. The prediction process starts by filling data about the appeared signs and symptoms by the user which are after that mapped with the CGDO ontology. Once the data are mapped, the prediction results are presented. The phase of prediction executes the rules which extract the predicted disease details based on the SWRL rule specified. The motivation behind the development of this system is to spread knowledge about the children diseases and their symptoms in a very simple way using the specialized social networking website www.emama.mk.
authors.affiliation: South East European University, Ilindenska n. 335, 1200 Tetovo, Macedonia.
authors.forename: Marika
authors.initials: M
authors.lastname: Apostolova Trpkovska
authors.affiliation: Norwegian University of Science and Technology, 2815 Gjøvik, Norway.
authors.forename: Sule
authors.initials: S
authors.lastname: Yildirim Yayilgan
authors.affiliation: South East European University, Ilindenska n. 335, 1200 Tetovo, Macedonia.
authors.forename: Adrian
authors.initials: A
authors.lastname: Besimi
mesh_terms: D064229:Biological Ontologies; D057225:Data Mining / Q000379:methods; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020407:Internet; D009323:Natural Language Processing; D012660:Semantics; D061108:Social Media; D012944:Social Support; D012984:Software