Abstract
Medicine has been a traditional domain for artificial intelligence (AI) research and application. It can be observed that the focus on expert systems (ES) in medicine in early days of AI has been changed to intelligent data analysis (IDA) in medicine, especially by machine learning and data mining techniques [Kononenko 01], [Lavrac et al. 97], [Cios 01]. At least, two reasons for the new trend are the bottleneck of knowledge acquisition and the explosive growth of medical databases. Intelligent data analysis in medicine has its own features because of the characteristics of medical data. These characteristics include the incompleteness (missing values), incorrectness (noise in data), sparseness (few and/or non-representable patient records available), and inexactness (inappropriate selection of parameters for a given task). Moreover, medical databases are characterized by the particular constraints and difficulties of the privacy-sensitive, heterogeneous, but voluminous, data of medicine [Cios and Moore 02].
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