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Application of genetic programming techniques to medical diagnosis

Marban O., Menasalvas E., Segovia J.

Journal of Applied Computer Science

2002

Vol. 10, nr 2

77-87

The computerization of scientific transactions and advances in data collection tools have contributed to obtain a large amount of clinical data online. Data Mining tools can find regularities, patterns and trends hidden in these data that will in turn assist the doctors when making decisions. In this paper we present a novel data mining technique, called Genetic Programming, and its application to diagnosis of breast cancer.

Minimal Decision Rules Based on the Apriori Algorithm

Fernandez M. C., Menasalvas E., Marban O., Pena J. M., Millan S.

International Journal of Applied Mathematics and Computer Science

2001

Vol. 11, no 3

691-704

Based on rough set theory many algorithms for rules extraction from data have been proposed. Decision rules can be obtained directly from a database. Some condition values may be unnecessary in a decision rule produced directly from the database. Such values can then be eliminated to create a more comprehensible (minimal) rule. Most of the algorithms that have been proposed to calculate minimal rules are based on rough set theory or machine learning. In our approach, in a post-processing stage, we apply the Apriori algorithm to reduce the decision rules obtained through rough sets. The set of dependencies thus obtained will help us discover irrelevant attribute values.