An implementation of a hybrid method based on machine learning to identify biomarkers in the Covid-19 diagnosis using DNA sequences

Abstract

Although some people do not have any chronic disease or are not in the risky age group for Covid-19, they are more vulnerable to the coronavirus. As the reason for this situation, some experts focus on the immune system of the person, while others think that the genetic history of patients may play a role. It is critical to detect corona from DNA signals as early as possible to determine the relationship between Covid-19 and genes. Thus, the effect on the severe course of the disease of variations in the genes associated with the corona disease will be revealed. In this study, a novel intelligent computer approach is proposed to identify coronavirus from nucleotide signals for the first time. The proposed method presents a multilayered feature extraction structure to extract the most effective features using an Entropy-based mapping technique, Discrete Wavelet Transform (DWT), statistical feature extractor, and Singular Value Decomposition (SVD), together. Then 94 distinctive features are selected by the ReliefF technique. Support vector machine (SVM) and k nearest neighborhood (k-NN) are chosen as classi-fiers. The method achieved the highest classification accuracy rate of 98.84% with an SVM classifier to detect Covid-19 from DNA signals. The proposed method is ready to be tested with a different database in the diagnosis of Covid-19 using RNA or other signals.