Detection of COVID-19 Patients using Speech Recognition with Support Vector Machine” and Comparing with “K Nearest Neighbour Algorithm”

This research endeavor is focused on identifying patients with the Covid-19 virus via the use of a novel voice recognition technique that makes use of a Support Vector Machine (abbreviated as “SVM”) and compares its accuracy with that of “K-Nearest Neighbor” (abbreviated as “KNN”). When it comes to speech recognition, the SVM method is regarded to be group 1, and the KNN method is considered to be group 2, and both groups have a total of 20 samples. The outcomes of these data were analyzed using statistical analysis using a”independent sample T-test,” which has a margin of error of 5% and a pretest power of 80%. At a significance of 0.042 (p 0.05), KNN obtains an accuracy of 87.5% whereas SVM achieves an accuracy of 96.5%. As compared to KNN, the prediction accuracy of Covid-19 employing SVM in novel voice recognition achieves much higher levels of accuracy.