Medical Reasoning with Rough-Set Influence Diagrams

There are several advantages to evaluating a problem using influence diagram operations. The analyst can use a representation that is natural to the decision maker, since the algorithm executes all of the inference and analysis automatically. The influence diagram solution procedure can also result in significant gains in efficiency. Conditional independence is clearly exhibited in the diagram, so the size of intermediate calculations can be reduced, resulting in considerable reductions in both processing time and memory requirements. However, when imprecise knowledge from data sets is involved in the systems, how to reason from approximate information becomes a main issue in evaluating influence diagrams effectively. This study develops an alternative knowledge model, rough-set influence diagrams (RSID), which combine rough-set decision rules and graphical structures of influence diagrams in medical settings. The proposed RSID provides a comprehensive schema for knowledge representation and decision support.