Evolving rule-based systems in two medical domains
using genetic programming
Artificial Intelligence in Medicine, 32 (3):
195--216 (November 2004)DOI: doi:10.1016/j.artmed.2004.02.007
Abstract
Summary Objective: To demonstrate and compare the
application of different genetic programming (GP) based
intelligent methodologies for the construction of
rule-based systems in two medical domains: the
diagnosis of aphasia's subtypes and the classification
of pap-smear examinations.
Material:
Past data representing (a) successful diagnosis of
aphasia's subtypes from collaborating medical experts
through a free interview per patient, and (b) correctly
classified smears (images of cells) by
cyto-technologists, previously stained using the
Papanicolaou method.
Methods:
Initially a hybrid approach is proposed, which combines
standard genetic programming and heuristic hierarchical
crisp rule-base construction. Then, genetic programming
for the production of crisp rule based systems is
attempted. Finally, another hybrid intelligent model is
composed by a grammar driven genetic programming system
for the generation of fuzzy rule-based
systems.
Results:
Results denote the effectiveness of the proposed
systems, while they are also compared for their
efficiency, accuracy and comprehensibility, to those of
an inductive machine learning approach as well as to
those of a standard genetic programming symbolic
expression approach.
Conclusion:
The proposed GP-based intelligent methodologies are
able to produce accurate and comprehensible results for
medical experts performing competitive to other
intelligent approaches. The aim of the authors was the
production of accurate but also sensible decision rules
that could potentially help medical doctors to extract
conclusions, even at the expense of a higher
classification score achievement.
