Depression Diagnosis Based on Ontologies and Bayesian Networks

Recently, depression become a general disease in the world due to the promotion of life quality and technology development. Most of people are not aware of the possibility of getting depressed himself in daily life. To accurately diagnose getting depressed becomes an important issue. In this paper, we utilize ontologies and Bayesian networks techniques to build the inference model for inferring the possibility of depression. We propose an ontology model to build the terminology of depression and utilize the Bayesian networks to infer the probability of depression. In addition, the paper also proposes an agent-based platform and addresses the implementation issue. The result shows that it can be well-inferring in the depression diagnosis.