囊性包虫病
荧光
包虫病
光谱(功能分析)
计算机科学
算法
医学
病理
物理
光学
量子力学
作者
Shu-Yong Xu,Wubulitalifu Dawuti,Maierhaba Maimaitiaili,Jingrui Dou,Malike Aizezi,Kalibixiati Aimulajiang,Xihong Lu,Guodong Lü
标识
DOI:10.1002/jbio.202300357
摘要
Abstract Cystic echinococcosis (CE) is a grievous zoonotic parasitic disease. Currently, the traditional technology of screening CE is laborious and expensive, developing an innovative technology is urgent. In this study, we combined serum fluorescence spectroscopy with machine learning algorithms to develop an innovative screening technique to diagnose CE in sheep. Serum fluorescence spectra of Echinococcus granulosus sensu stricto‐infected group ( n = 63) and uninfected E. granulosus s.s. group ( n = 60) under excitation at 405 nm were recorded. The linear support vector machine (Linear SVM), Quadratic SVM, medium radial basis function (RBF) SVM, K‐nearest neighbor (KNN), and principal component analysis‐linear discriminant analysis (PCA‐LDA) were used to analyze the spectra data. The results showed that Quadratic SVM had the great classification capacity, its sensitivity, specificity, and accuracy were 85.0%, 93.8%, and 88.9%, respectively. In short, serum fluorescence spectroscopy combined with Quadratic SVM algorithm has great potential in the innovative diagnosis of CE in sheep.
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