Deep Belief Network (DBN) Approach for Classification of COVID-19 Impact on People with Diabetes

Abstract

The World Health Organization (WHO) has announced the COVID-19 flare-up to be a general health crisis of global concern. In serious cases, COVID-19 can cause infection in the lungs (pneumonia), causes kidney malfunction and even demise. Individuals, under all ages will be contaminated by this harmful virus. Major cases of COVID-19 affected patients are normal with negligible influenza like no side effects. Some have gentle indications, more like a typical infection due to SARS-CoV-2 and has prompted severe sickness. Most of individuals who have come down with the infection have not should have been hospitalized for strong consideration. Elder people and people with previous ailments (like diabetes, coronary illness and asthma) give off an impression of being more unprotected against getting seriously sick with the COVID-19 infection. Specifically when people with diabetes adopted with a viral disease, it is essential  to treat with more attention and high care because of changes in blood glucose levels and, perhaps, the presence of diabetes complexities. The proposed DBN based methodology characterizes the effect of COVID 19 over diabetic patients with most regular indications of COVID-19 and wellbeing factors identified with diabetes. The model gets 98.86 % of accuracy during training and 97.81 % of accuracy during validation.