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With the proposed cardiac sound spectral analysis method, the high classification performances were achieved by 99.9% specificity and 99.5% sensitivity in classifying normal and abnormal sounds (heart disorders).
The classifier of SVM decision tree (SVM-DT) takes advantage of both the efficient computation of the tree architecture and the high classification accuracy of SVMs.
The high classification scores are likely to be enabled by the use of high density grids, and by the use of discrete phonemes.
The key issues of the proposed algorithm are how to build binary nearest neighbor search tree and design reduction strategies to keep the high classification accuracy patterns.
In all the four wavelet transforms, SVD postclassifier is the best suited one to achieve the high classification rate.
Thus, the high classification accuracy can be obtained when the within-class scatter is large due to big change of HRRPs.
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