College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830000, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2019 May 15;215:244-248. doi: 10.1016/j.saa.2019.02.063. Epub 2019 Feb 18.
This study presents a rapid and non-invasive method to screen high renin hypertension using serum Raman spectroscopy combined with different classification algorithms. The serum samples taken from 24 high renin hypertension patients and 22 non-high renin hypertension samples were measured in this experiment. Tentative assignments of the Raman peaks in the measured serum spectra suggested specific biomolecular changes between the groups. Principal component analysis (PCA) was first used for feature extraction and reduced the dimension of high-dimension spectral data. Then, support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (KNN) algorithms were employed to establish the discriminant diagnostic models. The accuracies of 93.5%, 93.5% and 89.1% were obtained from PCA-SVM, PCA-LDA and PCA-KNN models, respectively. The results from our study demonstrate that the serum Raman spectroscopy technique combined with multivariate statistical methods have great potential for the screening of high renin hypertension. This technique could be used to develop a portable, rapid, and non-invasive device for screening high renin hypertension.
本研究提出了一种使用血清拉曼光谱结合不同分类算法快速、无创地筛选高肾素高血压的方法。本实验中，对 24 名高肾素高血压患者和 22 名非高肾素高血压患者的血清样本进行了测量。对测量的血清光谱中的拉曼峰进行了试探性的归属，表明组间存在特定的生物分子变化。首先采用主成分分析（PCA）进行特征提取，降低了高维光谱数据的维度。然后，采用支持向量机（SVM）、线性判别分析（LDA）和 K 近邻（KNN）算法建立判别诊断模型。从 PCA-SVM、PCA-LDA 和 PCA-KNN 模型中分别得到 93.5%、93.5%和 89.1%的准确率。本研究结果表明，血清拉曼光谱技术结合多元统计方法在筛选高肾素高血压方面具有很大的潜力。该技术可用于开发一种便携式、快速、无创的高肾素高血压筛查设备。
