Non-invasive diagnosis of diseases using analysis of exhaled air are actively developing in medical practice. The aim of work is to compare a specifics groups of patients with pulmonary diseases. As features of the participants' state, absorption spectra of exhaled air samples were used. The analyzed spectrum is a bar chart which describes the dependence of the absorption coefficient on the wavelength. The problem to be solved is the choice of informative sub-ranges of spectra to improve the classification of the studied groups and the subsequent classification of several spectra for one person by voting methods. An integrated approach was used to solve this problem using the principal component analysis, support vector machine with RBF core and the subsequent voting technique.
Myocardial infarction (MI) causes partial or complete necrosis of the heart muscle. It means that muscle cells are wiped, and the contractility of the heart decreases. Today, for the MI diagnosis is based on the ECG recording or specific biomarkers identifying in the patient's blood, the most specific of which are: creatine phosphokinase (CPK), CPK-MB, fatty acids binding protein (H-FABP), myoglobin troponin-I, and troponin-T.
Additionally to these biomarkers in the MI patients’ blood, there are many other products of metabolism in damaged muscles, which are excreted from the body human body, including through exhaled air. The results of MI patients’ exhaled air analysis using photoacoustic laser spectroscopy and data mining are presented.
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