COVID Health Prediction Using Hybrid Additive Model

Abstract

The immense application and broad usage of Artificial Intelligence has incorporated it into all real-world domains. Now, with the increase in severity of globalpandemics, the usage of AI in healthcare has been increased. The Corona virus disease 2019, originated in China,has caused devastating effect on public-health and finance worldwide. It even caused in rise in number of deaths in the infected patients. Hence, quick prediction using AI algorithms and treatment of infection is required to evade increase in casualties. This paper proposed a hybrid Additive system, a mixture of the swarm intelligence for the selection of attributes and the prognosis employing the base model Additive Tree. It is assessed by applying several metrics like accuracy, sensitivity, count of decision nodes and features, specificity. It is then contrasted to other conventional approaches like Support Vector Machine, Hybrid Random Forest, and the base model as well. The projected prototype attained highest accuracy in detecting the health outcome of patient with 95.42% accuracy with just 8 features and 9 split nodes resulting in clear visual explanation for the medical practitioner.