Utilizing Fuzzy Multi-Attribute Decision Making for Group Clinical Decision Making Model

This research is aimed to build a Clinical Group Decision Support System model that is used to diagnose the mental disorder. This model makes use of the experts’ competence to give their preferences for some features related with the kinds of mental disorder. The knowledge base is built based on those preferences through preference aggregation process using Fuzzy MultiAttribute Decision Making (FMADM) steps. Aggregation proccess is used to make selection of some feature in a group of features and to make selection of the best alternative in a group of alternatives. Ordered Weighted Averaging (OWA) operator is used to do this feature preference aggregation. Importance Induced Ordered Weighted Averaging (I-IOWA) operator is used to do this alternative preference aggregation. Then Quantifier Guided Dominant Degree (QGDD) operator is used to determine the relevan features and the most possible kind of mental disorder. The inference process is used to diagnose the patient. Bayesian Belief Networks (BBN) is used to do the diagnosis process. Conditional probabitily is given from knowledge base and patient condition. This research has already been used to solve a CDSS case with 5 decision makers, 30 disorders, and 124 features. Finally, we have 635 knowledges in knowledge base.