The opinion of disease is important for Covid 19 as the antigen kit and RTPCR are unperfect and should be better for diagnosing such disease. Real-Time Return Transcription (real-time converse transcription – polymerase chain). Healthcare practices include the collection of various sorts of patient data to help the physician diagnose the patient's health. These data could be simple symptoms, first diagnosis by a doctor, or an in-depth laboratory test. These data are therefore used for analyses only by a doctor, who subsequently uses his particular medical skills to found the ailment. In order to classify Covid 19 disease datasets such mild, middle and severe diseases, the proposed model utilizes the notion of controlled machine education and GWO-optimization to regulate if the patient is affecting or not. An efficiency analysis is calculated and compared of disease data for both algorithms. The results of the simulations illustrate the effective nature and complexity of the data set for the grading techniques. Compared to SVM, the suggested model provides 7.8 percent improved prediction accuracy. The prediction accuracy is 8% better than the SVM. This results in an F1 score of 2 percent better than an SVM forecast.

Covid-19, Pneumonia, Machine Learning, Artificial Intelligence, Healthcare