Intellectual method of supporting decision making in a multi-parameter system of azimuthally invariant Mueller-polarimetric in pathologies assessment

The article presents a method for supporting decision-making in a multiparametric system of Muller-matrix diagnostics of biological layers based on statistical and wavelet analysis of a collection of azimuthal invariants of Muller-polarimetry and decision tree models to increase the accuracy of decisions. Training decision tree models based on minimization of the Gini index for informative features of the distributions of azimuthally independent invariants of the biological layer of the cervix are developed and the accuracy of pathology detection based on them is assessed. The experimental application of the improved PPR method in the differentiation of functional states of "normal" and "pathology" of the cervical muscle tissue of the uterine cervix with the measurement of ten distributions of azimuthal invariants of the Muller-polarimetric parameters of the uterine cervix has been demonstrated. An increase in the diagnostic accuracy of uterine cervix samples to the level of 97.2% has been achieved.