Development of Genetic Algorithm and Diagnosis Technıques for Covid-19 Virus Detection

Abstract

The rapid detection of the SARS-CoV-2 is a critical phase in preventing and controlling the covid-19 disease. This paper presents a novel approach for the diagnosis and analysis of SARS-CoV-2 gene samples at the early detection stage using a Multi-Objective Genetic Algorithm (MOGA). We apply MOGA and the diagnostic model to analyze and detect the genes of swabbing specimen nucleic acids, which are the most discriminative features of coronavirus. The Genetic Operators of MOGA are performed to search and diagnosis the suspected objects that have the same features of virus genes using the current population (samples swabs). In an early step, the amplification technique will be performed using the selective amplification of swabs to improve the diagnosing testing. In the next stage, MOGA will examine and explore the structure changes and new properties of SARS-CoV-2 using new fitness values to detect the nucleic acids that are performed by molecular diagnostic assays based on new chemical and physical characteristics of coved -19 swab. The proposed algorithm is an efficient technique to diagnose and explore gene mutations of the SARS-CoV-2 virus.