Dynamic knowledge validation and verification for CBR teledermatology system

Case-based reasoning has been of great importance in the development of many decision support applications. However, relatively little effort has gone into investigating how new knowledge can be validated. Knowledge validation is important in dealing with imperfect data collected over time, because inconsistencies in data do occur and adversely affect the performance of a diagnostic system.

This paper consists of two parts. First, it describes methods that enable the domain expert, who may not be familiar with machine learning, to interactively validate knowledge base of a Web-based teledermatology system. The validation techniques involve decision tree classification and formal concept analysis. Second, it describes techniques to discover unusual relationships hidden in the dataset for building and updating a comprehensive knowledge base, because the diagnostic performance of the system is highly dependent on the content thereof. Therefore, in order to classify different kinds of diseases, it is desirable to have a knowledge base that covers common as well as uncommon diagnoses.

Evaluation results show that the knowledge validation techniques are effective in keeping the knowledge base consistent, and that the query refinement techniques are useful in improving the comprehensiveness of the case base.