Comparison of genetic algorithms and other classification methods in the diagnosis of female urinary incontinence

Galactica, a newly developed machine-learning system that utilizes a geneti c 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 accurac y of classifiers, on the basis of patient data. The best methods were discr iminant analysis, logistic regression, C4.5 a nd Galactica. Practically no statistically significant differences existed between the prediction accura cy of these classification methods. We consider that machine-learning syste ms 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 near ly as well as C4.5, The results are in agreement with the results of earlie r research, indicating that genetic algorithms are a competitive method for constructing classifiers from medical data.