【摘 要】
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Diagnosis of the Graves\' ophthalmology remains a significant challenge.We identified between Graves\' ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy (LIBS)combined with machine learning method.In this work,th
【机 构】
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Wuhan National Laboratory for Optoelectronics,Huazhong University of Science and Technology,Wuhan 43
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Diagnosis of the Graves\' ophthalmology remains a significant challenge.We identified between Graves\' ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy (LIBS)combined with machine learning method.In this work,the paraffin-embedded samples of the Graves\' ophthalmol-ogy were prepared for LIBS spectra acquisition.The metallic elements (Na,K,A1,Ca),non-metallic element(O) and molecular bands ((C-N),(C-O)) were selected for diagnosing Graves\' ophthalmology.The selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis (LDA),support vector machine (SVM),k-nearest neighbor (kNN),and generalized regression neural network (GRNN),respectively.The results showed that the predicted accuracy rates of LDA,SVM,kNN,GRNN were 76.33%,96.28%,96.56%,and 96.33%,respectively.The sensitivity of four models were 75.89%,93.78%,96.78%,and 96.67%,respectively.The specificity of four models were 76.78%,98.78%,96.33%,and 96.00%,respectively.This demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves\' ophthalmopathy with a higher rate of accuracy.The kNN had the best performance by comparing the three nonlinear models.Therefore,LIBS combined with machine learning method can be an effective way to discriminate Graves\' ophthal-mology.
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