Improved discrimination of COVID-19 based on data enhancement technology and an information balance feature selection (INB) method

Abstract

The coronavirus disease (COVID-19) ravaged the world in late 2019 and posed a serious threat to human life and property destruction on a global scale. In this paper, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) method was selected for balancing the data sample, and an information balance feature selection (INB) method was first proposed to realize the accurate discrimination of COVID-19 saliva samples based on the attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. The results of the experiment showed that the INB method obtained higher classification accuracy than the traditional feature selection methods in both the original spectrum and the second-order derivative spectrum, especially in the second-order derivative spectrum where all the indexes reached about 85 %. In addition, the combination of WGAN_GP data augmentation and the INB method resulted in an accuracy of 88.7 % for the original spectrum and even 90.6 % for the second-order derivative spectrum. According to these findings, classification research using the WGAN_GP data enhancement model may increase classification accuracy. Additionally, the ability to successfully separate COVID-19 indicates that the INB method to identify spectral data features is a workable method, which also offers a fresh viewpoint on feature selection.