Analysis and Prediction of COVID-19 Disease using Machine Learning

In this research, the symptoms and other factors of a patient are utilized to train machine learning algorithms to predict whether the patient would die from or recover from (COVID19). It is probable that the coronavirus (COVID19) will create the highly infectious Coronavirus illness COVID19 (SARSCoV2). By coughing, sneezing, speaking, or inhaling, this virus can be transmitted from an infected person's lips and nose to small liquid particles. The size of these tiny atoms' inhalation droplets and aerosols vary, with bigger droplets being larger than smaller atoms. COVID19 is transferred by inhalation or by touching your eyes, nose, or mouth with your fingers after meeting a contaminated surface. When a big number of people are present, the COVID-19 virus can spread rapidly. We will need to examine the COVID19 dataset to see which models are the most accurate in estimating fatality rates for the virus's most vulnerable victims. Machine learning is used to compute and evaluate the performance of a variety of prediction models. We have used K-nearest neighbor (KNN), Support Vector Machine (SVM) classifier, Gaussian naive Bayesian (GNB), Decision tree (DT), and Logistic regression (LR) for the prediction of death and recovery of symptomatic patients. In this research, a variety of feature selection and extraction strategies were used, and prediction accuracy for feature selection methods for the KNN model and feature extraction methods for the GNB model both reached up to 96 percent. The k-nearest neighbor has performed and predicted high accuracy of 96% in both feature selection and extraction techniques.