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

被引:10
作者
Chen, Xiaomei [1 ,2 ]
Wu, Xue [1 ,2 ]
Chen, Chen [3 ]
Luo, Cainan [1 ,2 ]
Shi, Yamei [1 ,2 ]
Li, Zhengfang [1 ,2 ]
Lv, Xiaoyi [4 ]
Chen, Cheng [3 ]
Su, Jinmei [1 ,5 ]
Wu, Lijun [1 ,2 ]
机构
[1] Peoples Hosp Xinjiang Uygur Autonomous Reg, Dept Rheumatol & Immunol, Urumqi, Xinjiang, Peoples R China
[2] Xinjiang Clin Res Ctr Rheumatoid Arthrit, Urumqi, Xinjiang, Peoples R China
[3] Xinjiang Univ, Coll Software, Urumqi, Xinjiang, Peoples R China
[4] Xinjiang Univ, Coll Software, Key Lab Signal Detect & Proc, Urumqi, Xinjiang, Peoples R China
[5] Chinese Acad Med Sci & Peking Union Med Coll, Peking Union Med Coll Hosp, Dept Rheumatol & Clin Immunol, Beijing, Peoples R China
关键词
CLASSIFICATION; SERUM; SVM;
D O I
10.1038/s41598-023-29943-9
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The aim of this study was to explore the feasibility of Raman spectroscopy combined with computer algorithms in the diagnosis of primary Sjogren 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.
引用
收藏
页数:6
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