关键词:
Graves’ophthalmology
laser-induced breakdown spectroscopy(LIBS)
linear discriminant analysis(LDA)
support vector machine(SVM)
k-nearest neighbor(kNN)
generalized regression neural network(GRNN)
摘要:
Diagnosis of the Graves’ophthalmology remains a significant *** identified between Graves’ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy(LIBS)combined with machine learning *** this work,the paraffin-embedded samples of the Graves’ophthalmology were prepared for LIBS spectra *** metallic elements(Na,K,Al,Ca),non-metallic element(O)and molecular bands((C-N),(C-O))were selected for diagnosing Graves’*** selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis(LDA),support vector machine(SVM),k-nearest neighbor(ANN),and generalized regression neural network(GRNN),*** results showed that the predicted accuracy rates of LDA,SVM,ANN,GRNN were 76.33%,96.28%,96.56%,and 96.33%,*** sensitivity of four models were 75.89%,93.78%,96.78%,and 96.67%,*** specificity of four models were 76.78%,98.78%,96.33%,and 96.00%,*** demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves’ophthalmopathy with a higher rate of *** ANN had the best performance by comparing the three nonlinear ***,LIBS combined with machine learning method can be an effective way to discriminate Graves’ophthalmology.