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Different classification algorithms and serum surface enhanced Raman spectroscopy for noninvasive discrimination of gastric diseases
Author(s) -
Li Xiaozhou,
Yang Tianyue,
Li Siqi,
Wang Deli,
Song Youtao,
Yu Kedong
Publication year - 2016
Publication title -
journal of raman spectroscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.748
H-Index - 110
eISSN - 1097-4555
pISSN - 0377-0486
DOI - 10.1002/jrs.4924
Subject(s) - principal component analysis , linear discriminant analysis , pattern recognition (psychology) , support vector machine , artificial intelligence , raman spectroscopy , surface enhanced raman spectroscopy , chemistry , analytical chemistry (journal) , mathematics , computer science , chromatography , raman scattering , physics , optics
In this study, surface enhanced Raman spectroscopy (SERS) was used to investigate the spectral characteristics of blood serum for the purpose of diagnosing stomach diseases. SERS spectral data was collected from patients with atrophic gastritis, both pre‐operation and post‐operation gastric cancer, and from healthy individuals. Visual differences in the SERS spectra were observed between the four groups which indicate corresponding biomolecule concentration changes in blood. To further investigate the diagnostic ability of human serum, the spectral data was analyzed with three chemometric processes. These three methods extracted features and classified from the spectral data. Principal component analysis (PCA) was first performed to reduce the dimensionality of the original spectral data. Then, the classification methods support vector machine (SVM), linear discriminant analysis (LDA) and classification and regression tree (CART) were used for the evaluation of diagnostic ability. Accuracies of 96.5%, 88.8% and 87.1% were obtained for PCA‐SVM, PCA‐LDA and PCA‐CART, respectively. Copyright © 2016 John Wiley & Sons, Ltd.