Extracting Fuzzy Rules from Hierarchical Heterogeneous Neural Networks for Cardiovascular Diseases Diagnosis

Abstract

AbstractAlthough hierarchical fuzzy neural networks (FNNs) perform with high accuracy in medical diagnosis systems, their popularity is held back from well-known disadvantage of not providing explanation. This paper presents a novel rule extraction approach to extract accurate and comprehensible fuzzy IF-THEN rules via genetic algorithm (GA) from hierarchical heterogeneous FNNs (HHFNNs). When each sub-FNNs is constructed and trained, entire HHFNNs are constructed and trained jointly through integrating all trained sub-FNNs. The proposed rule extraction approach is used to extract rule set from each concerned sub-FNNs, all extracted rule sets are then combined as one set to provide automatically exclusive explanation to diagnostic conclusion when IF part contains input features and THEN part contains diagnostic conclusions. Experimental study on diagnosing three most common and important cardiovascular diseases using hospital site-measured data demonstrates that such proposed approach exhibits satisfactory explanation capability without concerning inner structures of HHFNNs.Keywordshierarchical heterogeneous fuzzy neural networksfuzzy logicrule extractiongenetic algorithmfuzzy IF-THEN rules