Diagnostic Support in Allergology Using Interactive Learning Classifier Systems

We present enhancements to learning classifier systems which make them capable of generating rules from case-based allergy test data. Iterated classification and the integration of experts’ suggestions mark important steps on the way to a system assisting allergologists in determining substances which are likely to be allergenes for a patient whose history is provided. After describing the requirements of this special classification problem we introduce the interactive learning classifier system as a powerful tool for these tasks and show some first practical results.