Raman spectroscopy combined with a support vector machine algorithm as a diagnostic technique for primary Sjögren’s syndrome

The aim of this study was to explore the feasibility of Raman spectroscopy combined with computer algorithms in the diagnosis of primary Sjögren syndrome (pSS). In this study, Raman spectra of 60 serum samples were acquired from 30 patients with pSS and 30 healthy controls (HCs). The means and stand...

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Vydáno v:Scientific reports Ročník 13; číslo 1; s. 5137 - 6
Hlavní autoři: Chen, Xiaomei, Wu, Xue, Chen, Chen, Luo, Cainan, Shi, Yamei, Li, Zhengfang, Lv, Xiaoyi, Chen, Cheng, Su, Jinmei, Wu, Lijun
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 29.03.2023
Nature Publishing Group
Nature Portfolio
Témata:
631/114/1305
631/114/1314
631/114/1386
639/638/11/872
692/699/249/1313
Algorithms
Diagnosis
Humanities and Social Sciences
Humans
multidisciplinary
Optimization
Principal Component Analysis
Principal components analysis
Raman spectroscopy
Science
Science (multidisciplinary)
Sjogren's syndrome
Sjogren's Syndrome - diagnosis
Spectroscopy
Spectrum analysis
Spectrum Analysis, Raman - methods
Support Vector Machine
Support vector machines
ISSN:2045-2322, 2045-2322
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Shrnutí:The aim of this study was to explore the feasibility of Raman spectroscopy combined with computer algorithms in the diagnosis of primary Sjögren syndrome (pSS). In this study, Raman spectra of 60 serum samples were acquired from 30 patients with pSS and 30 healthy controls (HCs). The means and standard deviations of the raw spectra of patients with pSS and HCs were calculated. Spectral features were assigned based on the literature. Principal component analysis (PCA) was used to extract the spectral features. Then, a particle swarm optimization (PSO)-support vector machine (SVM) was selected as the method of parameter optimization to rapidly classify patients with pSS and HCs. In this study, the SVM algorithm was used as the classification model, and the radial basis kernel function was selected as the kernel function. In addition, the PSO algorithm was used to establish a model for the parameter optimization method. The training set and test set were randomly divided at a ratio of 7:3. After PCA dimension reduction, the specificity, sensitivity and accuracy of the PSO-SVM model were obtained, and the results were 88.89%, 100% and 94.44%, respectively. This study showed that the combination of Raman spectroscopy and a support vector machine algorithm could be used as an effective pSS diagnosis method with broad application value.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-29943-9