Symbolic exposition of medical data-sets: a data mining workbench to inductively derive data-defining symbolic rules

Abstract

The application of data mining techniques to medical data is certainly beneficial for researchers interested in discerning the complexity of healthcare processes in real-life operational situations. We present a methodology, together with its computational implementation, for the automated extraction of data-defining CNF symbolic rules from medical data-sets comprising both annotated and un-annotated attributes. We propose a hybrid approach for symbolic rule extraction which features a sequence of methods including data clustering, data discretization and eventually symbolic rule discovery via rough set approximation. We present a generic data mining workbench that can generate cluster/class-defining symbolic rules from medical data, such that the resultant symbolic rules are directly applicable to medical rule-based expert systems.