Application of machine learning and laser optical-acoustic spectroscopy to study the profile of exhaled air volatile markers of acute myocardial infarction
Conventional acute myocardial infarction (AMI) diagnosis is quite accurate and has proved its
effectiveness. However, despite this, discovering more operative methods of this disease detection
is underway. From this point of view, the application of exhaled air analysis for a similar
diagnosis is valuable. The aim of the paper is to research effective machine learning algorithms for
the predictive model for AMI diagnosis constructing, using exhaled air spectral data. The target
group included 30 patients with primary myocardial infarction. The control group included 42 healthy
volunteers. The ‘LaserBreeze’ laser gas analyzer (Special Technologies Ltd, Russia), based on the
dual-channel resonant photoacoustic detector cell and optical parametric oscillator as the laser
source, had been used. The pattern recognition approach was applied in the same manner for the set
of extracted concentrations of AMI volatile markers and the set of absorption coefficients in a most
informative sp...
Source: Journal of Breath Research - Category: Respiratory Medicine Authors: Alexey V Borisov, Anna G Syrkina, Dmitry A Kuzmin, Vyacheslav V Ryabov, Andrey A Boyko, Olga Zaharova, Vyacheslav S Zasedatel and Yury V Kistenev Source Type: research
More News: Heart Attack | Learning | Respiratory Medicine | Russia Health | Study | Universities & Medical Training