Prediction of COVID-19 Using Recurrent Neural Network Model

Alamsyah Alamsyah, Budi Prasetiyo, M. Faris Al Hakim, Fadli Dony Pradana

Abstract


Purpose: The COVID-19 case that infected humans was first discovered in China at the end of 2019. Since then, COVID-19 has spread to almost all countries in the world. To overcome this problem, it takes a quick effort to identify humans infected with COVID-19 more quickly. Methods: In this paper, RNN is implemented using the Elman network and applied to the COVID-19 dataset from Kaggle. The dataset consists of 70% training data and 30% test data. The learning parameters used were the maximum epoch, learning late, and hidden nodes. Result: The research results show the percentage of accuracy is 88. Novelty: One of the alternative diagnoses for potential COVID-19 disease is Recurrent Neural Network (RNN).


Keywords


COVID-19, Recurrent Neural Network, Artificial Neural Network

Full Text:

PDF

References


M. H. Tayarani N., “Applications of artificial intelligence in battling against covid-19: A literature review,†Chaos, Solitons and Fractals, vol. 142, p. 110338, 2021, doi: 10.1016/j.chaos.2020.110338.

H. S. Maghded, K. Z. Ghafoor, A. S. Sadiq, K. Curran, D. B. Rawat, and K. Rabie, “A Novel AI-enabled Framework to Diagnose Coronavirus COVID-19 using Smartphone Embedded Sensors: Design Study,†Proc. - 2020 IEEE 21st Int. Conf. Inf. Reuse Integr. Data Sci. IRI 2020, pp. 180–187, 2020, doi: 10.1109/IRI49571.2020.00033.

V. Chamola, V. Hassija, V. Gupta, and M. Guizani, “A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact,†IEEE Access, vol. 8, no. April, pp. 90225–90265, 2020, doi: 10.1109/ACCESS.2020.2992341.

S. Varela-Santos and P. Melin, “A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks,†Inf. Sci. (Ny)., vol. 545, pp. 403–414, 2021, doi: 10.1016/j.ins.2020.09.041.

R. Vaishya, M. Javaid, I. H. Khan, and A. Haleem, “Artificial Intelligence (AI) applications for COVID-19 pandemic,†Diabetes Metab. Syndr. Clin. Res. Rev., vol. 14, no. 4, pp. 337–339, 2020, doi: 10.1016/j.dsx.2020.04.012.

A. Imran et al., “AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app,†Informatics Med. Unlocked, vol. 20, p. 100378, 2020, doi: 10.1016/j.imu.2020.100378.

M. Abdel-Basset, V. Chang, and N. A. Nabeeh, “An intelligent framework using disruptive technologies for COVID-19 analysis,†Technol. Forecast. Soc. Change, vol. 163, p. 120431, 2021, doi: 10.1016/j.techfore.2020.120431.

J. Rasheed et al., “A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic,†Chaos, Solitons and Fractals, vol. 141, p. 110337, 2020, doi: 10.1016/j.chaos.2020.110337.

O. S. Albahri et al., “Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects,†J. Infect. Public Health, vol. 13, no. 10, pp. 1381–1396, 2020, doi: 10.1016/j.jiph.2020.06.028.

M. E. H. Chowdhury et al., “Can AI help in screening viral and COVID-19 pneumonia?,†arXiv, vol. 8, pp. 132665–132676, 2020.

M. T. Rashid and D. Wang, “CovidSens: a vision on reliable social sensing for COVID-19,†Artif. Intell. Rev., vol. 54, no. 1, 2021, doi: 10.1007/s10462-020-09852-3.

J. Shuja, E. Alanazi, W. Alasmary, and A. Alashaikh, “COVID-19 open source data sets: A comprehensive survey,†medRxiv, 2020, doi: 10.1101/2020.05.19.20107532.

Y. Zhou, F. Wang, J. Tang, R. Nussinov, and F. Cheng, “Artificial intelligence in COVID-19 drug repurposing,†Lancet Digit. Heal., vol. 2, no. 12, pp. e667–e676, 2020, doi: 10.1016/S2589-7500(20)30192-8.

B. Prasetiyo, Alamsyah, and M. A. Muslim, “Analysis of building energy efficiency dataset using naive bayes classification classifier,†J. Phys. Conf. Ser., vol. 1321, no. 3, 2019, doi: 10.1088/1742-6596/1321/3/032016.

W. Walid and A. Alamsyah, “Recurrent Neural Network For Forecasting Time Series With Long Memory Pattern,†J. Phys. Conf. Ser., vol. 824, no. 1, 2017, doi: 10.1088/1742-6596/824/1/012038.

M. A. Muslim, I. Kurniawati, and E. Sugiharti, “Expert system diagnosis chronic kidney disease based on mamdani fuzzy inference system,†J. Theor. Appl. Inf. Technol., vol. 78, no. 1, pp. 70–75, 2015.

M. Mishra, V. Parashar, and R. Shimpi, “Development and evaluation of an AI System for early detection of Covid-19 pneumonia using X-ray (Student Consortium),†Proc. - 2020 IEEE 6th Int. Conf. Multimed. Big Data, BigMM 2020, pp. 292–296, 2020, doi: 10.1109/BigMM50055.2020.00051.

D. N. Vinod and S. R. S. Prabaharan, “Data science and the role of Artificial Intelligence in achieving the fast diagnosis of Covid-19,†Chaos, Solitons and Fractals, vol. 140, 2020, doi: 10.1016/j.chaos.2020.110182.

K. Hammoudi et al., “Deep learning on chest x-ray images to detect and evaluate pneumonia cases at the era of covid-19,†arXiv, pp. 1–6, 2020.

M. Ahmad, V. Tundjungsari, D. Widianti, P. Amalia, and U. A. Rachmawati, “Diagnostic decision support system of chronic kidney disease using support vector machine,†Proc. 2nd Int. Conf. Informatics Comput. ICIC 2017, vol. 2018-Janua, pp. 1–4, 2018, doi: 10.1109/IAC.2017.8280576.

S. S. Xu, M. W. Mak, and C. C. Cheung, “Deep neural networks versus support vector machines for ECG arrhythmia classification,†2017 IEEE Int. Conf. Multimed. Expo Work. ICMEW 2017, no. July, pp. 127–132, 2017, doi: 10.1109/ICMEW.2017.8026250.

D. Dansana et al., “Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm,†Soft Comput., vol. 0123456789, 2020, doi: 10.1007/s00500-020-05275-y.

Z. Che, S. Purushotham, K. Cho, D. Sontag, and Y. Liu, “Recurrent Neural Networks for Multivariate Time Series with Missing Values,†Sci. Rep., vol. 8, no. 1, pp. 1–12, 2018, doi: 10.1038/s41598-018-24271-9.




DOI: https://doi.org/10.15294/sji.v8i1.30070

Refbacks

  • There are currently no refbacks.




Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.