COVID-19 Pandemic analysis using SVM Classifier: Machine Learning in Health Domain

Abstract

Coronavirus has become very consistent in the medicinal field in recent months that has devastated many countries of the whole world in the last 5 -6 months. There is no exact cause of this disease, but research is still going on to find a proper treatment for this disease. Cases of coronavirus are increasing day by day, but there is not enough equipment available to identify the symptoms of the disease. This is why it takes a long time to identify the disease. Machine learning can help to reduce the time delay for the results of medical tests and to predict COVID-19 in patients. Detection of the corona (COVID-19) is now an important task for the physician. Corona spread among people so quickly and approaches 100,000 people worldwide. In this result, it is very important to identify the infected people then prevention of spread can be taken. This paper suggests an SVM-based methodology to detect coronavirus aimed at classification purposes. A set of procedures were recognized from works training including SVM (Support Vector Machine), RF (Random Forest), and ANN (Artificial Neural Network). Prediction of COVID-19 using machine learning may help grow the speed of virus detection as a result of decreased mortality. Analyzing the results obtained from the experiments, the Support Vector Machine (SVM) was identified to perform better than the other vectors.