JMIR Publications

ABSTRACT

Background:

Currently, hyperspectral technology has been used in various fields, but its applications for the detection of chylous plasma are lacking. This paper used hyperspectral techniques in combination with machine learning algorithms for the detection of chylous plasma, providing a new diagnostic method.

Objective:

This paper proposed a method of plasma chylous degree detection and recognition based on machine learning and hyperspectral technology. A plasma chylous degree detection model was established.It fills the gap of machine learning and hyperspectral technology in the detection of chylous plasma.

Methods:

The plasma hyperspectral data were preprocessed using the multiple scattering correction (MSC) method and then classified using four classification algorithms, including random forest (RF), K-nearest neighbor KNN), Perceptron, and stochastic gradient descent (SGD) algorithms and the best algorithm was compared.Finally, band selection is carried out to screen the best band subset.

Results:

The results showed that the random forest algorithm had the best effect. Then, the model of plasma chylous degree detection based on random forest was established. Finally, 10 important spectral bands, including 1192.45 nm, 1182.9 nm, 946.98 nm, 1202.01 nm, 1080.93 nm, 1278.41 nm, 1237.03 nm, 991.65 nm, 1020.35 nm, and 1697.8 nm, were selected by band selection. After adjusting the parameters to optimize the model, the prediction accuracy of the whole band was 0.89.

Conclusions:

This study suggested that hyperspectral technology could identify chylous plasma and could be used to improve its detection efficiency in biomedicine, human function tests, and other aspects.