Lexical modeling and weighted matrices for analyses of COVID-19 outbreak

The most dangerous and infectious disease, COVID-19, affecting millions of people is by an enveloped RNA virus known as SARS-COV-2 or Coronavirus, and the disease is unknown before the epidemic commenced in Wuhan, China, in December 2019. Many researchers are busy finding the vaccine for the pandemic. Here, we analyze the diagnostic methods by using mathematical modeling. The majority probable corona patient category with an enhanced AUC characterizes the SVM’s optimal diagnostics model in this chapter. Experimental and computational analyses demonstrate that the diagnosis of potentially COVID-19 can be supported by adopting ML algorithms that learn linguistic diagnostics from the interpretation of elderly persons. Highlight the collection of significant semantic, lexical, and top n-gram properties with the better ML method to estimate diseases. But diagnostics methods must be trained on massive datasets, leading to improved AUC and medical diagnoses of COVID-19 probability. A significant use resulting from mathematical modeling is that it claims transparency and accurateness about our model. These techniques can help in decision-making by useful predictions about substantial issues such as treatment protocols and interfere and minimize the spread of COVID-19.