Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19. (February 2023)

• Record Type: Journal Article
• Authors: Zhao, Bingqiang; Zhai, Honglin; Shao, Haiping; Bi, Kexin; Zhu, Ling
• Is Part Of: Computer methods and programs in biomedicine. Volume 229(2023)
• Publication Date: 2023-02
• Subjects: Vibrational spectroscopy -- Fourier transform infrared -- Raman scattering -- Tchebichef curve moments -- Grey wolf optimized support vector machine; COVID-19 coronavirus disease 2019 -- SI swarm intelligence -- GWO-SVM grey wolf optimized support vector machine -- RT-PCR real-time reverse transcription polymerase chain reaction -- CT computed tomography -- POC point-of-care -- FTIR Fourier transform infrared -- AI artificial intelligence -- ML machine learning -- LDA linear discriminative analysis -- k-NN k-nearest neighbors -- RF random forest -- NB Naïve Bayes -- SVM support vector machines -- RBF radical basis function -- GWO grey wolf optimization -- SG Savitzky-Golay -- MSC multiplicative scatter correction -- airPLS adaptive iteratively reweighted penalized least squares -- TCM Tchebichef curve moments -- FOM figures of merit -- AUROC area under the receiver operating characteristics curve -- AUPRC area under the precision-recall curve -- TP true positives -- TN true negatives -- FN false negatives -- FP false positives -- TPR true positive rate -- FPR false positive rate -- PRC precision-recall curve -- FNR false negative rate -- ACE2 angiotensin-converting enzyme 2; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107295
• Languages: English
• ISSNs: 0169-2607

Abstract

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