Genetic Programming for the Generation of Crisp and Fuzzy Rule Bases in Classification and Diagnosis of Medical Data

This paper demonstrates two methodologies for the construction of rule-based systems in medical decision making. The first approach consists of a method combining genetic programming and heuristic hierarchical rule-base construction. The second model is composed by a strongly-typed genetic programming system for the generation of fuzzy rule-based systems. Two different medical domains are used to evaluate the models. The first field is the diagnosis of subtypes of Aphasia. Two models for crisp rule-bases are presented. The first one discriminates between four major types and the second attempts the classification between all common types. A third model consisted of a GPgenerated fuzzy rule-based system is tested on the same domain. The second medical domain is the classification of Pap-Smear Test examinations where a crisp rulebased system is constructed. Results denote the effectiveness of the proposed systems. Comparisons on the system's comprehensibility and the transparency are included. These comparisons include for the Aphasia domain, previous work consisted of two neural network models.