Throat polyp detection based on compressed big data of voice with support vector machine algorithm

Abstract

Classification in large-scale data is a key problem in big data domain. The theory of compressive sensing enables the recovery of a sparse signal from a small set of linear, random projections which provides a compressive classification method operating directly on the compressed data without reconstructing for big data. In this paper, we collected the compressed vowel /a:/ and /i:/ voice signals using compressive sensing for throat polyp detection. The throat polyp prediction procedure based on wavelet packet transform and support vector machine intelligent algorithm was deduced. The experiments for throat polyp prediction with the proposed classification algorithm were carried out. The results showed that the correct rate of prediction was stable under different number of samples and different random measurement matrices. © 2014 Wang et al.