Detection of Chylous Plasma Based on Machine Learning and Hyperspectral Techniques

Abstract

Chylous blood is the main cause of unqualified and scrapped blood among volunteer blood donors. Therefore, a diagnostic method that can quickly and accurately identify chylous blood before donation is needed. In this study, the GaiaSorter “Gaia” hyperspectral sorter was used to extract 254 bands of plasma images, ranging from 900 nm to 1700 nm. Four different machine learning algorithms were used, including decision tree, Gaussian Naive Bayes (GaussianNB), perceptron, and stochastic gradient descent models. First, the preliminary classification accuracies were compared with the original data, which showed that the effects of the decision tree and GaussianNB models were better; their average accuracies could reach over 90%. Then, the feature dimension reduction was performed on the original data. The results showed that the effects of the decision tree were better with a classification accuracy of 93.33%. the classification of chylous plasma using different chylous indices suggested that the accuracies of the decision trees model both before and after the feature dimension reductions were the best with over 80% accuracy. The results of feature dimension reduction showed that the characteristic bands corresponded to all kinds of plasma, thereby showing their classification and identification potential. By applying the spectral characteristics of plasma to medical technology, this study suggested a rapid and effective method for the identification of chylous plasma and provided a reference for the blood detection technology to achieve the goal of reducing wasting blood resources and improving the work efficiency of the medical staff.