Wu Xue, Chen Chen, Chen Xiaomei, Luo Cainan, Lv Xiaoyi, Shi Yamei, Yang Jie, Meng Xinyan, Chen Cheng, Su Jinmei, Wu Lijun
Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 830001, China; Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi 830001, China.
College of Information Science and Engineering, Xinjiang University, Urumqi 830001, China.
Photodiagnosis Photodyn Ther. 2022 Dec;40:103057. doi: 10.1016/j.pdpdt.2022.103057. Epub 2022 Aug 6.
Interstitial lung disease (ILD) is a major complication of Primary Sjögren's syndrome (pSS) patients.It is one of the main factors leading to death. The aim of this study is to evaluate the value of serum Raman spectroscopy combined with machine learning algorithms in the discriminatory diagnosis of patients with Primary Sjögren's syndrome associated with interstitial lung disease (pSS-ILD).
Raman spectroscopy was performed on the serum of 30 patients with pSS, 28 patients with pSS-ILD and 30 healthy controls (HC). First, the data were pre-processed using baseline correction, smoothing, outlier removal and normalization operations. Then principal component analysis (PCA) is used to reduce the dimension of data. Finally, support vector machine(SVM), k nearest neighbor (KNN) and random forest (RF) models are established for classification.
In this study, SVM, KNN and RF were used as classification models, where SVM chooses polynomial kernel function (poly). The average accuracy, sensitivity, and precision of the three models were obtained after dimensionality reduction. The Accuracy of SVM (poly) was 5.71% higher than KNN and 6.67% higher than RF; Sensitivity was 5.79% higher than KNN and 8.56% higher than RF; Precision was 6.19% higher than KNN and 7.45% higher than RF. It can be seen that the SVM (poly) had better discriminative effect. In summary, SVM (poly) had a fine classification effect, and the average accuracy, sensitivity and precision of this model reached 89.52%, 91.27% and 89.52%, respectively, with an AUC value of 0.921.
This study demonstrates that serum RS combined with machine learning algorithms is a valuable tool for diagnosing patients with pSS-ILD. It has promising applications.
间质性肺疾病（ILD）是原发性干燥综合征（pSS）患者的主要并发症。它是导致死亡的主要因素之一。本研究的目的是评估血清拉曼光谱结合机器学习算法在原发性干燥综合征合并间质性肺疾病（pSS-ILD）患者鉴别诊断中的价值。
对30例pSS患者、28例pSS-ILD患者和30名健康对照者（HC）的血清进行拉曼光谱检测。首先，使用基线校正、平滑、去除异常值和归一化操作对数据进行预处理。然后采用主成分分析（PCA）对数据进行降维。最后，建立支持向量机（SVM）、k近邻（KNN）和随机森林（RF）模型进行分类。
本研究以SVM、KNN和RF作为分类模型，其中SVM选择多项式核函数（poly）。降维后得到了三种模型的平均准确率、灵敏度和精确率。SVM（poly）的准确率比KNN高5.71%，比RF高6.67%；灵敏度比KNN高5.79%，比RF高8.56%；精确率比KNN高6.19%，比RF高7.45%。可见SVM（poly）具有更好的鉴别效果。综上所述，SVM（poly）具有良好的分类效果，该模型的平均准确率、灵敏度和精确率分别达到89.52%、91.27%和89.52%，AUC值为0.921。
本研究表明血清拉曼光谱结合机器学习算法是诊断pSS-ILD患者的一种有价值的工具。它具有广阔的应用前景。
