Analysis Of Pattern Change In Diseases Using Machine Learning

Abstract

SARS-CoV-2, also known as COVID-2019, is spreading quickly and threatening our society on a global scale. Every government is employing money, infrastructure, data security equipment, survival evaluations, treatments, and other resources to make major attempts to stop the fatal disease from spreading. Nationwide, COVID-19 sepsis infections are spreading quickly. To allocate resources and classify patients in order to avert death, a reliable and accessible clinical assessment of the severity of human illness is essential. Through the analysis of genome and protein sequences, the study seeks to confirm an early stage of sickness by estimating the likelihood of further alterations depending on their length. A study involving 852 isolates of the COVID-19 Next strain application was conducted. The project included a dashboard showing the distribution and percentages of gene mutations for each base sequence in the genomic data, a transmission chart, and a visual phylogenetic analysis. Additionally, it took basic traits from genetic alterations that might help anticipate more mutations in the future. For 11191 patients, survival analysis parameters were found.