Exploring Machine Learning Pipelines for Raman Spectral Classification of COVID-19 Samples

Abstract

Raman Spectroscopy can analyze and identify the chemical compositions of samples. This study aims to develop a computational method based on machine learning algorithms to classify Raman spectra of serum samples from COVID-19 infected and non-infected human subjects. The method can potentially serve as a tool for rapid and accurate classification of COVID-19 versus non-COVID-19 patients and toward a direction for biomarker discoveries in research. Different machine learning classifiers were compared using pipelines with different dimensionality reduction and scaler techniques. The performance of each pipeline was investigated by varying the associate parameters. Assessment of dimensionality reduction application suggests that the pipelines generally performed better when the number of components does not exceed 50. The LightGBM model with ICA and MMScaler applied, yielded the highest test accuracy of 98.38% for pipelines with dimensionality reduction while the SVM model with MMScaler applied yielded the highest test accuracy of 96.77% for pipelines without dimensionality reduction. This study shows the effectiveness of Raman spectroscopy to classify COVID-19-induced characteristics in serum samples.