TITLE:
COVID-19 Virus Prediction Using CNN and Logistic Regression Classification Strategies
AUTHORS:
Asadi Srinivasulu, Tarkeshwar Barua, Srinivas Nowduri, Madhusudhana Subramanyam, Sivaram Rajeyyagari
KEYWORDS:
Machine Learning, COVID-19 Virus, Deep Learning, ANN, CNN and LR
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.10 No.1,
February
28,
2022
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.