Application of XGBoost Algorithm in The Detection of SARS-CoV-2 Using Raman Spectroscopy. Issue 1 (January 2021) - Zeng, Wandan; Wang, Qi; Xia, Zhiping; Li, Zhiping; Qu, Han
Abstract: The novel coronavirus (SARS-CoV-2), which was first discovered in late 2019 and rapidly spread to many countries around the world in a short period of time, is highly contagious and poses a significant threat to global public safety. How to quickly and efficiently detect whether a human is infected by the novel coronavirus is a crucial step in dealing with this public health emergency. Therefore, the collected Raman spectral data are preprocessed by normalization, smoothing denoising and feature extraction in this paper. A novel coronavirus detection method based on XGBoost and Raman spectroscopy is proposed. The experiments demonstrated the feasibility and accuracy of the method in the detection of novel coronavirus with an accuracy of 93.548%.