Mathematical Analysis of Raman Spectra Data Arrays Using Machine Learning Algorithms

This paper is devoted to the application of mathematical methods of classification and differentiation of low-resolution spectral data arrays of Raman light scattering for complex biological compounds as human platelets. Spectral data arrays consisted of 1266 spectra from 4 groups of patients, totaling 152 people were analyzed. A random forest algorithm was used. Potential biomarkers of differences between patient groups were identified, on which the given algorithms were tested. Using the random forest algorithm for classification of spectra of healthy patients without therapy and patients with cardiovascular pathologies without therapy, we have achieved the accuracy of 83.4%. Classification of the healthy patients on and off therapy shows the accuracy of 76.26% and classification of the patients with cardiovascular pathologies shows 70% accuracy.