Abstract
A class features generating algorithm using discriminant analysis approaches is offered to improve the performance of an eye disease diagnostic computer system. A method to evaluate the informativeness of a set of features is described. The support vector machine is used in the research to prove the efficiency of using the features in classification of fundus patterns. The algorithm is universal enough to be applied to improve the informativeness of any combination of features.
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