Применение машинного обучения для диагностики некоторых социально значимых заболеваний по выдыхаемому человеком воздуху методом инфракрасной лазерной спектроскопии
Year: 2023
Type: article
Abstract: The infrared spectra of the air exhaled by several groups of volunteers were studied: those suffering from type 1 diabetes, bronchial asthma, and pneumonia. To record infrared spectra, a tunable quantum-cascade laser (QCL) was used. QCL emits in the wavelength range from 5.3 to 12.8 μm in a pulsed mode with a pulse width of 50 ns, a power of up to 150 mW, and a tuning step of 1 cm-1. The laser is optically coupled to an astigmatic gas cell of the Herriot type with an optical path length of 76 m. A difference was found in the intensity of selective lines of biomarker molecules in the spectra of exhaled air of healthy volunteers compared to similar indicators of volunteers suffering from a certain disease. For an example of methods such as the support vector machine (SVM), the k-nearest neighbors (k-NN) and the random forest algorithm (RandomForest), the possibility of classifying volunteers by the infrared spectra of their exhaled air is shown. The use of dimensionality reduction methods (PCA and t-SNE) made it possible to increase the accuracy of disease classification up to 98% in terms of the accuracy metric.
Source: Оптика и спектроскопия
Institution: Bauman Moscow State Technical University
Language: Russian
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4384789986