Discrimination of the Specific Gravity of Urine Using Spectrophotometry by the Parallel Connection of Two Modified Feature Selection Methods

Spectroscopy has become prominent in medical surveillance due to its low cost, speed, and nondestructive testing. However, the issue of class unbalance in medical data causes existing algorithms to favor the majority classes, leading to their malfunction. This study attempts to propose a parallel type method based on two modified feature selection methods to achieve visible spectral discrimination of unbalanced urine specific gravity (USG) data. Firstly, the root mean square error (RMSE) of successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were modified by increasing weight coefficients. Then, SPA, CARS, modified SPA (mSPA), modified CARS (mCARS), tandem connection of SPA and CARS (CARS-SPA), tandem connection of mSPA and CARS (mCARS mSPA), parallel connection of SPA and CARS (CARS + SPA), and parallel connection of mSPA and CARS (mCARS + mSPA) were used to select characteristic wavelengths from the full spectrq. Finally, based on the variable subsets extracted by each method, the random forest (RF) models were established to verify the performance of the parallel strategy and modification method. The results showed that the RF model of mCARS + mSPA achieved effective discrimination of USG with high accuracy (92.81%), high sensitivity (0.9270), and high resolution (0.9280). It means that a parallel hybrid based on two modified feature selection methods can effectively select feature wavelengths beneficial for minority class recognition, achieving the mining of spectral features of unbalanced data. At the same time, this study also provides a novel example of the strategy of parallel feature selection methods.