Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release
Shawni Dutta, Samir Kumar Bandyopadhyay
Abstract
Introduction: Corona Virus Infectious Disease (COVID-19) is the infectious disease. The COVID-19 disease came to earth in early 2019. It is expanding exponentially throughout the world and affected an enormous number of human beings starting from the last month. The World Health Organization (WHO) on March 11, 2020 declared COVID-19 was characterized as “Pandemic”. This paper proposed approach for confirmation of COVID-19 cases after the diagnosis of doctors. The objective of this study uses machine learning method to evaluate how much predicted results are close to original data related to Confirmed-Negative-Released-Death cases of COVID-19.
Materials and methods: For this purpose, a verification method is proposed in this paper that uses the concept of Deep-learning Neural Network. In this framework, Long shrt-term memory (LSTM) and Gated Recurrent Unit (GRU) are also assimilated finally for training the dataset. The prediction results are tally with the results predicted by clinical doctors.
Results: The results are obtained from the proposed method with accuracy 87 % for the “confirmed Cases”, 67.8 % for “Negative Cases”, 62% for “Deceased Case” and 40.5 % for “Released Case”. Another important parameter i.e. RMSE shows 30.15% for Confirmed Case, 49.4 % for Negative Cases, 4.16 % for Deceased Case and 13.72 % for Released Case.
Conclusions: The outbreak of Coronavirus has the nature of exponential growth and so it is difficult to control with limited clinical persons for handling a huge number of patients within a reasonable time. So it is necessary to build an automated model, based on machine learning approach, for corrective measure after the decision of clinical doctors.
Keywords
References
1. World Health Organization. WHO Statement Regarding Cluster of Pneumonia Cases in Wuhan, China. Available from: https://www.who.int/china/news/detail/09-01-2020-who-statement-regardingcluster-of-pneumonia-cases-in-wuhan-china.
2. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497-506. doi: 10.1016/S0140-6736(20)30183-5.
3. Anastassopoulou C, Russo L, Tsakris A, Siettos C. Data-Based analysis, modelling and forecasting of the COVID-19 outbreak. PLoS One. 2020;15(3):e0230405. doi: 10.1371/journal.pone.0230405.
4. Hu Z, Ge Q, Li S, Jin L, Xiong M. Artificial Intelligence Forecasting of Covid-19 in China. 2020;1–20. Available from: https://arxiv.org/ftp/arxiv/papers/2002/2002.07112.pdf.
5. Fong SJ, Li G, Dey N, Gonzalez-Crespo R, Herrera-Viedma E. Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak. Int J Interact Multimed Artif Intell. 2020;(6(1):1322-40.
6. Sokolova M, Japkowicz N, Szpakowicz S. Beyond Accuracy , F-Score and ROC: A family of discriminant measures for performance evaluation. In: Sattar A, Kang BH, editors. AI 2006: Advances in Artificial Intelligence: 19th Australian Joint Conference on Artificial Intelligence, Hobart, Australia, December 4-8, 2006. Proceedings. Heidelberg: Springer;2006:1015-21.
7. Tjandra A, Sakti S, Manurung R, Adriani M, Nakamura S. Gated Recurrent Neural Tensor Network. doi: 10.1109/IJCNN.2016.7727233.
8. Bouktif S, Fiaz A, Ouni A, Serhani MA. Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies. 2018;11(7: 1636. doi: https://doi.org/10.3390/en11071636.
9. Chung J, Gulcehre C, Cho K, Bengio Y. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. aRxiv. 2014. Available from: https://arxiv.org/abs/1412.3555.
10. Tang Y, Huang Y, Wu Z, Meng H, Xu M, Cai L. Question detection from acoustic features using recurrent neural network with gated recurrent unit. Available from: 10.1109/ICASSP.2016.7472854.
11. Datarist. Coronavirus-Dataset Version 1[Data file]. KCDC (Korea Centers for Disease Control and Prevention). Available from: https://www.kaggle.com/kimjihoo/coronavirusdataset-old.
Submitted date:
04/24/2020
Reviewed date:
05/07/2020
Accepted date:
05/09/2020
Publication date:
05/12/2020
Facebook
Google+
Twitter
LinkedIn
Mendeley
StumbleUpon
CiteULike
Reddit
Email