Machine learning assisted rapid approach for quantitative prediction of biochemical parameters of blood serum with FTIR spectroscopy.

Journal: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
PMID: 39418681

Abstract

This study develops regression models for predicting blood biochemical data using Fourier-transform infrared spectroscopy (FTIR) analysis. Absorption at specific wavelengths of blood serum is revealed to have strong correlations with biochemical parameters, such as ALT, amylase, AST, protein, bilirubin, Gamma-GT, iron, calcium, uric acid, triglycerides, phosphatase and cholesterol, were shown. The results consistently demonstrate that Random Forest Regression outperforms other models, delivering impressive outcomes for the majority of the analyzed parameters. For some parameters we obtained a coefficient of determination of 0.95 and more (amylase, AST, iron, calcium, protein, uric acid and cholesterol), which makes this approach to be applicable in clinical diagnostics. These findings highlight the potential of FTIR analysis combined with regression models for precise assessment of blood biochemistry.

Authors

  • O G Chechekina
    Institute of Spectroscopy, Russian Academy of Sciences, 108840 Troitsk, Russia; National Research University Higher School of Economics, 101000 Moscow, Russia.
  • E V Tropina
    Institute of Spectroscopy, Russian Academy of Sciences, 108840 Troitsk, Russia; National Research University Higher School of Economics, 101000 Moscow, Russia.
  • L I Fatkhutdinova
    School of Physics and Engineering, ITMO University, 197101 St. Petersburg, Russia.
  • M V Zyuzin
    School of Physics and Engineering, ITMO University, 197101 St. Petersburg, Russia.
  • A A Bogdanov
    School of Physics and Engineering, ITMO University, 197101 St. Petersburg, Russia; Qingdao Innovation and Development Center, Harbin Engineering University, 266000 Qingdao, China.
  • Y Ju
    Advanced Research Institute of Multidisciplinary, Beijing Institute of Technology, 100081 Beijing, China.
  • K N Boldyrev
    Institute of Spectroscopy, Russian Academy of Sciences, 108840 Troitsk, Russia; National Research University Higher School of Economics, 101000 Moscow, Russia; Advanced Research Institute of Multidisciplinary, Beijing Institute of Technology, 100081 Beijing, China. Electronic address: kn.boldyrev@gmail.com.

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