Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19. (February 2023) - Zhao, Bingqiang; Zhai, Honglin; Shao, Haiping; Bi, Kexin; Zhu, Ling
Highlights: Proposed a GWO-SVM model based on swarm intelligence optimization algorithms for screening of COVID-19 patients via biofluid vibrational spectroscopy. Trained the model on the dataset prepared by collecting vibrational spectroscopy from publicly available databases. GWO-SVM model outperforms some other machine learning algorithms. GWO-SVM model showed faster convergence while ensuring the classification performance compared with GS-SVM. Promising results indicate that the chance of vibrational spectroscopy coupled with GWO-SVM model to help COVID-19 diagnosis and have the potential be further used for early screening of other infectious diseases. Abstract: Background and Objective: Efforts to alleviate the ongoing coronavirus disease 2019 (COVID-19) crisis showed that rapid, sensitive, and large-scale screening is critical for controlling the current infection and that of ongoing pandemics. Methods: Here, we explored the potential of vibrational spectroscopy coupled with machine learning to screen COVID-19 patients in its initial stage. Herein presented is a hybrid classification model called grey wolf optimized support vector machine (GWO-SVM). The proposed model was tested and comprehensively compared with other machine learning models via vibrational spectroscopic fingerprinting including saliva FTIR spectra dataset and serum Raman scattering spectra dataset. Results: For the unknown vibrational spectra, the presented GWO-SVM model provided an accuracy, specificity and F1_score value of 0.9825, 0.9714 and 0.9778 for saliva FTIR spectra dataset, respectively, while an overall accuracy, specificity and F1_score value of 0.9085, 0.9552 and 0.9036 for serum Raman scattering spectra dataset, respectively, which showed superiority than those of state-of-the-art models, thereby suggesting the suitability of the GWO-SVM model to be adopted in a clinical setting for initial screening of COVID-19 patients. Conclusions: Prospectively, the presented vibrational spectroscopy based GWO-SVM model can facilitate in screening of COVID-19 patients and alleviate the medical service burden. Therefore, herein proof-of-concept results showed the chance of vibrational spectroscopy coupled with GWO-SVM model to help COVID-19 diagnosis and have the potential be further used for early screening of other infectious diseases. Graphical Abstract: Image, graphical abstract