A belief rule-based expert system to assess mental disorder under uncertainty

Mental disorder is a change of mental or behavioral pattern that causes sufferings and impairs the ability to function in ordinary life. In psychopathology, the assessment methods of mental disorder contain various types of uncertainties associated with signs and symptoms. This study identifies a method that addresses the issue of uncertainty in assessing mental disorder. The fuzzy logic knowledge representation schema can address uncertainty associated with linguistic terms including ambiguity, imprecision, and vagueness. However, fuzzy logic is incapable of addressing uncertainty due to ignorance, incompleteness, and randomness. So, a belief rule-based expert system (BRBES) has been designed and developed with the capability of handling the uncertainties mentioned. Evidential reasoning works as the inference engine and the belief rule base as the knowledge representation schema in this BRBES. The study shows that the results generated by BRBES are more reliable than that of Fuzzy Rule-based expert system and from a human expert.