Blood hyperviscosity identification with reflective spectroscopy of tongue tip based on principal component analysis combining artificial neural network

Abstract

BackgroundWith spectral methods, noninvasive determination of blood hyperviscosity in vivo is very potential and meaningful in clinical diagnosis. In this study, 67 male subjects (41 health, and 26 hyperviscosity according to blood sample analysis results) participate.MethodsReflectance spectra of subjects’ tongue tips is measured, and a classification method bases on principal component analysis combined with artificial neural network model is built to identify hyperviscosity. Hold-out and Leave-one-out methods are used to avoid significant bias and lessen overfitting problem, which are widely accepted in the model validation.ResultsTo measure the performance of the classification, sensitivity, specificity, accuracy and F-measure are calculated, respectively. The accuracies with 100 times Hold-out method and 67 times Leave-one-out method are 88.05% and 97.01%, respectively.ConclusionsExperimental results indicate that the built classification model has certain practical value and proves the feasibility of using spectroscopy to identify hyperviscosity by noninvasive determination.

Identification of foodborne pathogenic bacteria using confocal Raman microspectroscopy and chemometrics

“…About 30% of the samples are assigned to the testing set, and there are 160 samples in the testing set. They were used to test the predictions of the models ( Liu et al, 2018 ). Leave-one-out cross-validation (LOOCV) technique is used for the cross-validation of FDA model, and it can examine the quality of the classifier and avoid overfitting.…”