COVID-19Disease Diagnosis using Artificial Intelligence based on Gene Expression: A Review

Abstract

    Clinical symptoms of COVID-19, which can cause pulmonary inflammation, are varied. The use of microarray technology to examine variations in gene expression in particular organisms has become a popular new trend in genetic research, which can be used for disease detection and prediction. Large numbers of unprocessed raw gene expression profiles can occasionally make it difficult to choose dataset attributes and categorize them into the proper group or class, which can lead to computational and analytical challenges. Using the whole set of genes makes getting acceptable COVID-19 classification accuracy difficult due to the oversized dimensions, noise in the gene expression data and tiny sample sizes. This review thoroughly assesses microarray COVID-19 illness investigations, emphasizing techniques for selecting features. Hence, COVID-19 could be controlled with the development of a very sensitive and accurate point-of-care COVID-19 detection device, and this review thus offers general recommendations for researchers and workers in the biosensor field.