Method of Disease Diagnosing Based on SVM and Rough Set

To solve the problem that large training samples and slow speed in diagnosing based on support vector classifier, a hybrid classification algorithm applying attribute reduction of rough set and classification principles of SVM to diagnose diseases was proposed. RS was used to preprocess the attributes on condition that no effective information was lost. Redundant attributes and conflicting objects from decision table was deleted. And the dimension and complexity in the process of SVM classification was reduced. Then it classifies and forecasts objects by SVM classifier, so as to achieve the diagnosis for case. Experiments show that it can improve the rate of diagnosis under the reasonable reducing accurate rating after RS reducing information, and can improve the speed of diagnosing diseases when SVM dealing with much disease information.