Combined numerical and linguistic knowledge representation and its application to medical diagnosis

In this study, we propose a novel hybrid intelligent system (HIS) which provides a unified integration of numerical and linguistic knowledge representations. The proposed HIS is a hierarchical integration of an incremental learning fuzzy neural network (ILFN) and a linguistic model, i.e., fuzzy expert system (FES), optimized via the genetic algorithm (GA). The ILFN is a self-organizing network with the capability of fast, one-pass, online, and incremental learning. The linguistic model is constructed based on knowledge embedded in the trained ILFN or provided by the domain expert. The knowledge captured from the low-level ILFN can be mapped to the higher-level linguistic model and vice versa. The GA is applied to optimize the linguistic model to maintain high accuracy, comprehensibility, completeness, compactness, and consistency. After the system being completely constructed, it can incrementally learn new information in both numerical and linguistic forms. To evaluate the system's performance, the well-known benchmark Wisconsin breast cancer data set was studied for an application to medical diagnosis. The simulation results have shown that the proposed HIS perform better than the individual standalone systems. The comparison results show that the linguistic rules extracted are competitive with or even superior to some well-known methods.