Аннотация:
The processing of diagnostic data in pulmonology is complicated for a doctor due to the need
to analyze many indicators, the relationships between which can be complex, and the degree
of influence on the diagnostic result can be different. Traditionally, general clinical,
biochemical, and questionnaire methods are used to support making a diagnosis. They allow
describing the state of the bronchopulmonary system by a variety of indicators. The modern
practice of laser correlation spectroscopy makes it possible to expand various indicators. Still,
their values are represented by sets of one-dimensional distribution diagrams and are not
convenient for analysis. Therefore, we investigated the feasibility of classifying the values of
32 biophysical indicators obtained by laser correlation spectroscopy in this work. We first
performed data visualization and found that the classes of diagnosed diseases did not have a
clear separation but were separated from the normal state. We then examined the results of
classifying the data using three algorithms – naive Bayes, logistic regression, and random
forest. We conclude that the most appropriate algorithm is logistic regression. The work value
lies in expanding the set of diagnostic indicators due to the high-precision results of the
classification of biophysical indicators, which increases the objectivity of the diagnosis of
pulmonological diseases.
