COVID-19 antibody level analysis with feature selection approach

Wiesław Paja; Krzysztof Pancerz; Catalin Stoean

doi:10.1016/j.procs.2022.09.490

COVID-19 antibody level analysis with feature selection approach

Procedia Comput Sci. 2022:207:4268-4275. doi: 10.1016/j.procs.2022.09.490. Epub 2022 Oct 19.

Authors

Wiesław Paja¹, Krzysztof Pancerz², Catalin Stoean³

Affiliations

¹ College of Natural Sciences, University of Rzeszów, Rzeszów, Poland.
² Institute of Technology and Computer Science, Academy of Zamosc, Poland.
³ University of Craiova, Department of Computer Science, Craiova, Romania.

Abstract

The study presented here considers the analysis of a medical dataset for the identification of the stage of onset of COVID-19 coronavirus. These data, presented in previous work by the authors, have been subjected to extensive analysis and additional calculations. The data were obtained by analyzing blood samples of infected individuals at 1, 3, and 6 months after COVID-19 infection. Results were obtained from FTIR spectrometry experiments. The results indicate a very effective ability to identify the different states of infection, and between 1 and 6 months even perfect. Specific spectrometry wavelength ranges can also be distinguished as medical markers.

Keywords: COVID-19; FTIR; Fourier Transform Infrared spectrometry; computer aided medical diagnosis; feature selection.