International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 13, No. 3, 8 Jun. 2021
Cover page and Table of Contents: PDF (size: 1833KB)
Visualization & Prediction of COVID-19 Future Outbreak by Using Machine Learning
Full Text (PDF, 1833KB), PP.16-32
Views: 0 Downloads: 0
Author(s)
Ahmed Hassan Mohammed Hassan
1,*
Arfan Ali Mohammed Qasem
1
Walaa Faisal Mohammed Abdalla
1
Omer H. Elhassan
2
Index Terms
COVID-19, Deep Neural Network, Support Vector Machines, Bayesian Network, Polynomial Regression Pandemic
Abstract
Day by day, the accumulative incidence of COVID-19 is rapidly increasing. After the spread of the Corona epidemic and the death of more than a million people around the world countries, scientists and researchers have tended to conduct research and take advantage of modern technologies to learn machine to help the world to get rid of the Coronavirus (COVID-19) epidemic. To track and predict the disease Machine Learning (ML) can be deployed very effectively. ML techniques have been anticipated in areas that need to identify dangerous negative factors and define their priorities. The significance of a proposed system is to find the predict the number of people infected with COVID-19 using ML. Four standard models anticipate COVID-19 prediction, which are Neural Network (NN), Support Vector Machines (SVM), Bayesian Network (BN) and Polynomial Regression (PR). The data utilized to test these models content of number of deaths, newly infected cases, and recoveries in the next 20 days. Five measures parameters were used to evaluate the performance of each model, namely root mean squared error (RMSE), mean squared error (MAE), mean absolute error (MSE), Explained Variance score and r2 score (R2). The significance and value of proposed system auspicious mechanism to anticipate these models for the current scenario of the COVID-19 epidemic. The results showed NN outperformed the other models, while in the available dataset the SVM performs poorly in all the prediction. Reference to our results showed that injuries will increase slightly in the coming days. Also, we find that the results give rise to hope due to the low death rate. For future perspective, case explanation and data amalgamation must be kept up persistently.
Cite This Paper
Ahmed Hassan Mohammed Hassan, Arfan Ali Mohammed Qasem, Walaa Faisal Mohammed Abdalla, Omer H. Elhassan, "Visualization & Prediction of COVID-19 Future Outbreak by Using Machine Learning", International Journal of Information Technology and Computer Science(IJITCS), Vol.13, No.3, pp.16-32, 2021. DOI:10.5815/ijitcs.2021.03.02
Reference
[1]C. Sohrabi et al., "World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19)," 2020.
[2]S. K. Mohanty et al., "Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and coronavirus disease 19 (COVID-19)–anatomic pathology perspective on current knowledge," vol. 15, no. 1, pp. 1-17, 2020.
[3]A. Rajkomar, J. Dean, and I. J. N. E. J. o. M. Kohane, "Machine learning in medicine," vol. 380, no. 14, pp. 1347-1358, 2019.
[4]A. Jakaria, M. M. Hossain, and M. A. J. a. p. a. Rahman, "Smart weather forecasting using machine learning: a case study in tennessee," 2020.
[5]D. S. Hain and R. Jurowetzki, "The promises of Machine Learning and Big Data in entrepreneurship research," in Handbook of quantitative research methods in entrepreneurship: Edward Elgar Publishing, 2020.
[6]M. Arıtürk, S. Yavuz, and T. J. M. M. i. I. S. Allahviranloo, "Artificial Intelligence and Autonomous Car," pp. 391-412, 2020.
[7]Smriti Ayushi, V R Badri Prasad, " Cross-Domain Recommendation Model based on Hybrid Approach", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.11, pp. 36-42, 2018.DOI: 10.5815/ijmecs.2018.11.05
[8]B. Nazlı, Y. Gültepe, and H. Altural, "Classification of Coronary Artery Disease Using Different Machine Learning Algorithms," 2020.
[9]G. Battineni, G. G. Sagaro, N. Chinatalapudi, and F. J. J. o. P. M. Amenta, "Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis," vol. 10, no. 2, p. 21, 2020.
[10]M. Maniruzzaman, M. J. Rahman, B. Ahammed, M. M. J. H. I. S. Abedin, and Systems, "Classification and prediction of diabetes disease using machine learning paradigm," vol. 8, no. 1, p. 7, 2020.
[11]S. Barik, S. Mohanty, D. Rout, S. Mohanty, A. K. Patra, and A. K. Mishra, "Heart Disease Prediction Using Machine Learning Techniques," in Advances in Electrical Control and Signal Systems: Springer, 2020, pp. 879-888.
[12]R. Y. Kumbhar and S. J. S. i. I. P. N. Vijaykumar, "Breast Cancer Prediction Using Machine Learning Algorithm," vol. 40, no. 68, pp. 380-395, 2020.
[13]Q. Li et al., "Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia," 2020.
[14]Mirza Waseem Hussain, Tabasum Mirza, Malik Mubasher Hassan. " Impact of COVID-19 Pandemic on the Human Behavior ", International Journal of Education and Management Engineering (IJEME), Vol.10, No.5, pp.35-61, 2020. DOI: 10.5815/ijeme.2020.05.05
[15]M. P. Kelly, "Digital technologies and disease prevention," American journal of preventive medicine, vol. 51, no. 5, pp. 861-863, 2016.
[16]S. Mahmoud, M. Hussein, and A. Keshk, "Predicting Future Products Rate using Machine Learning Algorithms," International Journal of Intelligent Systems & Applications, vol. 12, no. 5, 2020.
[17]J. H. U. D. Repository. (2020, 4/OTC). COVID-19 Data. Available: https://github.com/CSSEGISandData/COVID-19
[18]T. M. Mitchell, "Machine Learning, volume 1 of 1," ed: McGraw-Hill Science/Engineering/-Math, 1997.
[19]G. E. J. M. L. P. Hinton and M. P. Methods", "Connectionist learning procedures. Artificial Intelligence, 40 1-3: 185 234, 1989. Reprinted in J. Carbonell, editor,"" 1990.
[20]X. Glorot and Y. Bengio, "Understanding the difficulty of training deep feedforward neural networks," in Proceedings of the thirteenth international conference on artificial intelligence and statistics, 2010, pp. 249-256.
[21]K. He, X. Zhang, S. Ren, and J. Sun, "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification," in Proceedings of the IEEE international conference on computer vision, 2015, pp. 1026-1034.
[22]D. P. Kingma and J. J. a. p. a. Ba, "Adam: A method for stochastic optimization," 2014.
[23]C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 1995/09/01 1995.
[24]D. J. J. N. c. MacKay, "Bayesian interpolation," vol. 4, no. 3, pp. 415-447, 1992.
[25]M. E. J. J. o. m. l. r. Tipping, "Sparse Bayesian learning and the relevance vector machine," vol. 1, no. Jun, pp. 211-244, 2001.
[26]H. Huang, M. J. A. A. Abdel-Aty, and Prevention, "Multilevel data and Bayesian analysis in traffic safety," vol. 42, no. 6, pp. 1556-1565, 2010.