Abstract: Aiming at the problem that the current feature selection algorithm was applied to the data classification accuracy was not ideal, we proposed a feature selection algorithm based on maximum correlation and minimum redundancy. The algorithm combined feature selection algorithm and clustering analysis algorithm to process the feature, and eliminated redundant features in the classification. We used support vector machine (SVM) to classify the data obtained from a group of patients with heart disease. The experimental results show that this method can effectively screen the features that affect classification, and then improve the classification accuracy.

Key words: clustering, classification, support vector machine, feature selection