An Ontology-based Modeling for Classifying Risk of Suicidal Behavior

Classifying an individual with suicidal behavior is a complex problem. A clinical decision support system (CDSS) helps medical experts in their daily work and supports them in effective decision-making. The huge amount of medical information and the complex correlation between the risk factors and the level of risk for suicidal behavior makes the representation of data is challenging. Therefore, this paper proposes an ontology-based modeling to classify an individual with at-risk of suicidal behavior for effective clinical decision support system. The case study is conducted to evaluate the ontology model and provides a general approach to knowledge sharing and reusing knowledge for suicide risk prevention and management. The finding shows that the ontology model can be used as a knowledge base for classification, and it is suitable to capture medical knowledge, detailed concepts, and relationships in a formal way using Web Ontology Language (OWL). The results of the proposed ontology model in terms of accuracy, specificity, and sensitivity are 83%, 84%, and 82% respectively.