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

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.