宫颈癌
支持向量机
径向基函数核
高斯函数
核(代数)
癌症
医学
人工智能
表面增强拉曼光谱
多项式核
拉曼光谱
模式识别(心理学)
计算机科学
核方法
高斯分布
数学
内科学
光学
拉曼散射
化学
物理
计算化学
组合数学
作者
Xiangxiang Zheng,Guohua Wu,Jing Wang,Longfei Yin,Xiaoyi Lv
摘要
In this study, we investigated the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with a support vector machine (SVM) algorithm to discriminate hysteromyoma and cervical cancer from healthy volunteers rapidly. SERS spectra of serum samples were recorded from 30 hysteromyoma patients, 36 cervical cancer patients as well as 30 healthy subjects. SVM was used to establish the classification models, and three types of kernel functions, namely linear, polynomial, and Gaussian radial basis function (RBF), were utilized for comparison. When the polynomial kernel function was employed, the overall diagnostic accuracy for classifying the three groups could achieve 86.5%. In addition, when the optimal kernel function was selected, the diagnostic accuracy for identifying healthy versus hysteromyoma, healthy versus cervical cancer, and hysteromyoma versus cervical cancer reached 98.3%, 93.9%, and 90.9%, respectively. The current results indicate that serum SERS technology, together with the SVM algorithm, is expected to become a clinical tool for rapid screening of hysteromyoma and cervical cancer.
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