Covid-19 analysis by using machine and deep learning

被引:0
作者
Yadav D. [1 ]
Maheshwari H. [2 ]
Chandra U. [3 ]
Sharma A. [4 ]
机构
[1] Department of Computer Science and Technology, Glocal University, Saharanpur, UP
[2] Department of Computer Science and Engineering, Uttarakhand Technical University, Dehradun, Uttarakhand
[3] Department of Computer Science, Banda University of Agriculture and Technology, Banda, UP
[4] Maharishi Markandeshwar Deemed to be University Mullana, Ambala, Haryana
来源
Studies in Big Data | 2020年 / 80卷
关键词
ARIMA; COVID-19; Deep learning; LSTM; Machine learning; SARS-CoV; SVM;
D O I
10.1007/978-981-15-8097-0_2
中图分类号
学科分类号
摘要
Coronavirus is the pandemic in the whole world due to infection spread with community transfer. World Health Organization renames coronavirus to COVID-19, and the full name of coronavirus is severe acute respiratory syndrome coronavirus (SARS-CoV). COVID-19 belongs to the Betacoronavirus family and affects the respiratory system of humans. Machine learning is the part of artificial intelligence which used existing machine learning algorithms and dataset pattern to find an adequate solution for the problem. This chapter used machine learning algorithms supervised and unsupervised to analyze the spreading pattern such as confirmed case, recovered case, and death case of coronavirus worldwide. Analysis of the infection rate and mortality rate of coronavirus in the top 10 countries of the world. Analysis the effect of coronavirus in Asia and Europe visualized the result on the map. Choropleth is a thematic map which divides the world geographical area into different colors on the base of the data variable. This chapter used machine learning algorithms such as support vector machine, navies Bayes theorem, linear regression, decision tree repressors, random forest, and prophet algorithm for future prediction and to test the accuracy of prediction. The analysis of confirmed, recovered, and death cases is visualized by using Matplotlib in Python. Time series analysis of recovered, confirmed, and death cases of coronavirus predicted the future infection of the virus on the world. Analysis of the effect of Coronavirus, Country-wise, and State wise on top infected countries. Deep learning algorithm, long short-term memory (LSTM), is a recurrent neural network-type algorithm used to predict the future infection rate of coronavirus in the world. In this study, different machine learning algorithms were implemented and find the algorithm which gives the highest accuracy. Bar graph and pie are used to visualize the result of the experiment by using Python which helps to better understand the result. Analysis of the effect of coronavirus on Russia, Italy, the USA, the UK, Iran, Turkey, Germany, France, and Brazil finds the infection rate, mortality rate, and recovery rate of these countries. Analyze the precision, recall, and f1-score of these algorithms, and plot the graph. Find the recovered and death rates per one thousand patients of confirmed cases. © Springer International Publishing AG 2018.
引用
收藏
页码:31 / 63
页数:32
相关论文
共 51 条
[1]  
Su S., Wong G., Shi W., Liu J., Lai A.C., Zhou J., Et al., Epidemiology, genetic recombination, and pathogenesis of coronaviruses, Trends in Microbiology, 24, 6, pp. 490-502, (2016)
[2]  
Zhu N., Zhang D., Wang W., Li X., Yang B., Song J., Et al., A novel coronavirus from patients with pneumonia in China, 2019, New England J. Med., 382, 8, pp. 727-733, (2020)
[3]  
Estola T., Coronaviruses, a new group of animal RNA viruses, Avian Dis, 14, 2, pp. 330-336, (1970)
[4]  
Fabricant J., The early history of infectious bronchitis, Avian Dis, 42, 4, pp. 648-650, (1998)
[5]  
McIntosh K., Coronaviruses: A comparative review, current topics in microbiology and immunology. Ergebnisse der Mikrobiologie und Immunitätsforschung, statistical linked, 87, (1974)
[6]  
Kahn J.S., McIntosh K., History and recent advances in coronavirus discovery, Pediatric Infect. Dis. J., 24, 11, pp. S223-S227, (2005)
[7]  
Mahase E., The BMJ in 1965, BMJ, 369, (2020)
[8]  
Tyrrell D.A., Bynoe M.L., Cultivation of viruses from a high proportion of patients with colds, Lancet, 1, pp. 76-77, (1966)
[9]  
Hamre D., Procknow J.J., A new virus isolated from the human respiratory tract, Proc. Socexpbiol. Med., 121, pp. 190-193, (1966)
[10]  
McIntosh K., Dees J.H., Becker W.B., Kapikian A.Z., Chanock R.M., Recovery in tracheal organ cultures of novel viruses from patients with respiratory disease, Proc. Natl. Acad. Sci. U.S.A., 57, pp. 933-940, (1967)