Classification of patients with broncho-pulmonary diseases based on analysis of absorption spectra of exhaled air samples with SVM and neural network algorithm application

Yu. V. Kistenev; D. A. Kuzmin; D. A. Vrazhnov; A. V. Borisov

doi:10.1117/12.2249130

29 November 2016 Classification of patients with broncho-pulmonary diseases based on analysis of absorption spectra of exhaled air samples with SVM and neural network algorithm application

Yu. V. Kistenev, D. A. Kuzmin, D. A. Vrazhnov, A. V. Borisov

Author Affiliations +

Proceedings Volume 10035, 22nd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics; 1003507 (2016) https://doi.org/10.1117/12.2249130
Event: XXII International Symposium Atmospheric and Ocean Optics. Atmospheric Physics, 2016, Tomsk, Russian Federation

Abstract

In this work results of classification of patients with broncho-pulmonary diseases based on analysis of exhaled air samples are presented. These results obtained by application of laser photoacoustic spectroscopy method and intellectual data analysis ones (Principal Component Analysis, Support vector machines, neural networks). Absorption spectra of exhaled air of gathered volunteers were registered; data preparation for classification procedure of absorption spectra of exhaled air of healthy and sick people was made. Also error matrices for neural networks and sensitivity/specificity values in case of classification with SVM method were obtained. This work was partially supposed by the Federal Target Program for Research and Development, Contract No. 14.578.21.0082 (unique identifier of applied scientific research and experimental development RFMEFI57814X0082).

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Yu. V. Kistenev, D. A. Kuzmin, D. A. Vrazhnov, and A. V. Borisov "Classification of patients with broncho-pulmonary diseases based on analysis of absorption spectra of exhaled air samples with SVM and neural network algorithm application", Proc. SPIE 10035, 22nd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 1003507 (29 November 2016); https://doi.org/10.1117/12.2249130

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