Abstract
Galactica, a newly developed machine-learning system that utilizes a genetic algorithm
for learning, was compared with discriminant analysis, logistic regression, k-means
cluster analysis, a C4.5 decision-tree generator and a random bit climber hill-climbing
algorithm. The methods were evaluated in the diagnosis of female urinary incontinence
in terms of prediction accuracy of classifiers, on the basis of patient data. The
best methods were discriminant analysis, logistic regression, C4.5 and Galactica.
Practically no statistically significant differences existed between the prediction
accuracy of these classification methods. We consider that machine-learning systems
C4.5 and Galactica are preferable for automatic construction of medical decision aids,
because they can cope with missing data values directly and can present a classifier
in a comprehensible form. Galactica performed nearly as well as C4.5. The results
are in agreement with the results of earlier research, indicating that genetic algorithms
are a competitive method for constructing classifiers from medical data.
Keywords
Urinary Incontinence - Computer-Assisted Diagnosis - Genetic Algorithms - Machine
Learning - Comparison
