Can knowledge discovery from databases enhance case-based reasoning for medical diagnosis support systems?

Case-based reasoning (CBR) which involves the representation of prior experience as cases, provides a natural approach for developing a medical diagnosis support system because medical practitioners usually solve new problems by comparing them to previously seen cases. We propose a general framework for such a system with the aim to assess the normality and abnormality of the cervical spine. Two distinct types of visual features are used for indexing the cases: a small set of basic features that is known to be useful by radiologists for diagnosis and a minimal set of salient generic visual features. The latter is obtained by using knowledge discovery from databases (KDD) techniques. Standard image analysis techniques are used to automatically extract both of these types of features from the images. The efficiency and adaptiveness of the system can be further improved by using KDD techniques to reduce the set of cases minimum as well as to extract appropriate adaptation rules.