Nonparametric Kernel-Based Distribution Modeling of Bioelectrical Impedance Features for Breast Tissue Classification

LU Meng; WU Yunfeng

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Nonparametric Kernel-Based Distribution Modeling of Bioelectrical Impedance Features for Breast Tissue Classification

LU Meng 1,WU Yunfeng 2 * #

1.School of Information Science and Technology, Xiamen University, XiaMen 361005

2.School of Information Science and Technology, Xiamen University, Xiamen 361005

*Correspondence author

#Submitted by

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Funding: Fundamental Research Funds for the Central Universities of China （No.No. 2010121061）, Natural Science Foundation of Fujian Province of China （No.No. 2011J01371）

Opened online: 6 May 2013

Accepted by: none

Citation: LU Meng,WU Yunfeng.Nonparametric Kernel-Based Distribution Modeling of Bioelectrical Impedance Features for Breast Tissue Classification[OL]. [ 6 May 2013] http://en.paper.edu.cn/en_releasepaper/content/4540663

Classification of breast tissues helps assess early stage pathological conditions in the cancerating breast. In this paper, we present a nonparametric modeling method to estimate the bivariate probability densities of features for the normal and pathological breast tissues. Two representative bioelectrical features were first selected for classification by using the Kruskal-Wallis test and correlation analysis. The bivariate feature density was estimated using Gaussian kernels, and the nonlinear classification was performed using the maximal posterior probability method. The results showed that the kernel-based maximal posterior probability (KMPP) classification provided an accurate rate of 84.91% and the area under receiver operating characteristic (ROC) curve of 0.9307. The diagnostic performance and the nonlinear decision boundary of the proposed KMPP method were better than Fisher's linear discriminant analysis (accuracy: 83.02%, area under ROC curve: 0.8789).

Keywords:Pattern recognition; Breast tissue; Kenerl density estimation

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