Even if some people don't have any chronic illnesses or don't fall within the Covid-19 risk age range, they are more susceptible to the coronavirus. Some specialists believe that the patient's immune system is to blame, while others believe that the patient's genetic background may be a factor. To ascertain the connection between Covid-19 and genes, it is crucial to identify corona from DNA signals as early as feasible. As a result, it will be possible to determine how changes in the corona disease-related genes affect the disease's severe course. This study proposes a revolutionary intelligent computer method for the first time to distinguish coronavirus from nucleotide signals. The suggested approach offers a multi-layered Singular Value Decomposition, Discrete Wavelet Transform statistical feature extractor, and Entropy-based mapping approach are combined to create a feature extraction framework to extract the most potent features. The Relief approach then chooses distinguishing characteristics. The classifiers used are support vector machine and k closest neighbourhood (k-NN). The technique identified Covid-19 from DNA signals with the greatest classification accuracy rate of 98.84% using an SVM classifier. The suggested technique for determining Covid-19 using RNA or other signals is prepared to be tested with a different database.
KeywordsCovid-19; Big data analysis; Machine learning; Linear algebra; Biomedical signal processing
Published Date: 2023-02-28; Received Date: 2023-02-02
