Abstract
We propose a constrained-syntax genetic programming
(GP) algorithm for discovering classification rules in
medical data sets. The proposed GP contains several
syntactic constraints to be enforced by the system
using a disjunctive normal form representation, so that
individuals represent valid rule sets that are easy to
interpret. The GP is compared with C4.5, a well-known
decision-tree-building algorithm, and with another GP
that uses Boolean inputs (BGP), in five medical data
sets: chest pain, Ljubljana breast cancer, dermatology,
Wisconsin breast cancer, and pediatric adrenocortical
tumour. For this last data set a new preprocessing step
was devised for survival prediction. Computational
experiments show that, overall, the GP algorithm
obtained good results with respect to predictive
accuracy and rule comprehensibility, by comparison with
C4.5 and BGP.
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