Combining a neural network with case-based reasoning in a diagnostic system

This paper presents a new approach for integrating case-based reasoning (CBR) with a neural network (NN) in diagnostic systems. When solving a new problem, the neural network is used to make hypotheses and to guide the CBR module in the search for a similar previous case that supports one of the hypotheses. The knowledge acquired by the network is interpreted and mapped into symbolic diagnosis descriptors, which are kept and used by the system to determine whether a final answer is credible, and to build explanations for the reasoning carried out. The NN-CBR model has been used in the development of a system for the diagnosis of congenital heart diseases (CHD). The system has been evaluated using two cardiological databases with a total of 214 CHD cases. Three other well-known databases have been used to evaluate the NN-CBR approach further. The hybrid system manages to solve problems that cannot be solved by the neural network with a good level of accuracy. Additionally, the hybrid system suggests some solutions for common CBR problems, such as indexing and retrieval, as well as for neural network problems, such as the interpretation of the knowledge stored in a neural network and the explanation of reasoning.