Application of the Combination of Capillary Electrophoresis with Wavelet Neural Network in the Assisted Clinical Diagnosis of Breast Cancer

Abstract

Abstract: Thirteen kinds of normal and modified nucleosides were determined in urine samples from 46 healthy persons and 26 breast cancer patients by capillary electrophoresis. Awavelet neural network model has been used as a powerful pattern recognition tool to distinguish breast cancer patients from healthy persons. The recognition rate for the training set reached to 100% and above 96% of people in the predicting set were correctly classified. Compared with standard backpropagation neural network, wavelet neural network had stronger abilities of information extraction and approximation. The results also demonstrated that the predicting ability of wavelet neural network was higher than those of principal component analysis and linear discriminant analysis. The combination of capillary electrophoresis and wavelet neural network was expected to be an assisted tool for the clinical diagnosis of breast cancer.

Key words: Wavelet neural network, Neural network, Capillary electrophoresis, Nucleosides, Urine

CLC Number:

O657

TrendMD:

XIONG Jian-Hui, ZHENG Yu-Fang, ZHANG Pu-Dun, SHI Xian-Zhe, YANG Jun, ZHANG Yu-Kui, XU Guo-Wang. Application of the Combination of Capillary Electrophoresis with Wavelet Neural Network in the Assisted Clinical Diagnosis of Breast Cancer[J]. Chem. J. Chinese Universities, 2003, 24(5): 803.

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Application of the Combination of Capillary Electrophoresis with Wavelet Neural Network in the Assisted Clinical Diagnosis of Breast Cancer

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