COVID-19 Virus Prediction Using CNN and Logistic Regression Classification Strategies

Abstract

COVID-19 virus is certainly considered as one of the harmful viruses amongst all the illnesses in biological science. COVID-19 symptoms are fever, cough, sore throat, and headache. The paper gave a singular function for the prediction of most of the COVID-19 virus diseases and presented with the Convolutional Neural Networks and Logistic Regression which might be the supervised learning and gaining knowledge of strategies for most of COVID-19 virus diseases detection. The proposed system makes use of an 8-fold pass determination to get a correct result. The COVID-19 virus analysis dataset is taken from Microsoft Database, Kaggle, and UCI websites gaining knowledge of the repository. The proposed studies investigate Convolutional Neural Networks (CNN) and Logistic Regression (LR) about the usage of the UCI database, Kaggle, and Google Database Datasets. This paper proposed a hybrid method for COVID-19 virus, most disease analyses through reducing the dimensionality of capabilities the usage of Logistic Regression (LR), after which making use of the brand new decreased function dataset to Convolutional Neural Networks and Logistic regression. The proposed method received the accuracy of 78.82%, sensitiveness of 97.41%, and specialness of 98.73%. The overall performance of the proposed system is appraised thinking about performance, accuracy, error rate, sensitiveness, particularity, correlation and coefficient. The proposed strategies achieved the accuracy of 78.82% and 97.41% respectively through Convolutional Neural Networks and Logistic Regression.