MACHINE LEARNING-BASED RNN-LSTM FOR PREDICTING COVID-19 CASES IN MALAYSIA
DOI:
https://doi.org/10.46754/jmsi.2024.12.004Keywords:
COVID-19, Machine Learning, Endemic Period, SVR, RNN-LSTMAbstract
Started in the beginning of January 2020, the world still struggles to cope with the spread of COVID-19, including in Malaysia. Although the world has invented some vaccines for the disease, the coronavirus problem is still there and perhaps it will be a big issue until a few years ahead. This study will look at the trend of the spread on COVID-19 in Malaysia after two years since the pandemic haunted human life, particularly when the pandemic turned into endemic. We developed a model prediction based on Recurrent Neural Networks (RNN) with Long-Short Term Memory (LSTM) to predict the COVID-19 outbreak in both pandemic and endemic periods. In addition, the COVID-19 classes are split into susceptible (S), exposed (E), infectious (I), and recovered(R). We then forecast 60 days ahead by using these two models which are RNN with long-short term memory (LSTM) and a support vector regression (SVR). The results prove that both methods have different advantages. SVR can perform better in predicting the pandemic period, while RNN-LSTM has better predict the endemic period. From the results, it can be said that SVR is more appropriate for predicting dynamic curves, while RNN-LSTM is suitable for smooth curves. Thus, the results show that the studied prediction technique is a promising and encouraging procedure for studying the future trends of the COVID-19.
References
Fernandez-Nieto, D., Jimenez-Cauhe, J., Suarez-Valle, A., Moreno-Arrones, O. M., Saceda-Corralo, D., Arana-Raja, A., & Ortega-Quijano, D. (2020). Characterization of acute acral skin lesions in nonhospitalized patients: A case series of 132 patients during the COVID-19 outbreak. Journal of the American Academy of Dermatology, 83(1), e61-e63. https://doi.org/10.1016/j.jaad.2020.04.093
Zaidi, S. a. J., Tariq, S., & Belhaouari, S. B. (2021). Future prediction of COVID-19 vaccine trends using a voting classifier. Data, 6(11), Article 112. https://doi.org/10.3390/data6110112
Zoabi, Y., Deri-Rozov, S., & Shomron, N. (2021). Machine learning-based prediction of COVID-19 diagnosis based on symptoms. NPJ Digital Medicine, 4(1), Article 3. https://doi.org/10.1038/s41746-020-00372-6
Yang, L., Liu, S., Liu, J., Zhang, Z., Wan, X., Huang, B., Chen, Y., & Zhang, Y. (2020). COVID-19: immunopathogenesis and Immunotherapeutics. Signal Transduction and Targeted Therapy, 5(1), Article 128. https://doi.org/10.1038/s41392-020-00243-2
Ciotti, M., Angeletti, S., Minieri, M., Giovannetti, M., Benvenuto, D., Pascarella, S., Sagnelli, C., Bianchi, M., Bernardini, S., & Ciccozzi, M. (2019). COVID-19 outbreak: An overview. Chemotherapy, 64(5-6), 215-223. https://doi.org/10.1159/000507423
Elengoe, A. (2020). COVID-19 outbreak in Malaysia. Osong Public Health and Research Perspectives, 11(3), 93-100. https://doi.org/10.24171/j.phrp.2020.11.3.08
Cao, X. (2020). COVID-19: Immunopathology and its implications for therapy. Nature Reviews. Immunology, 20(5), 269-270. https://doi.org/10.1038/s41577-020-0308-3
Du, Y., Tu, L., Zhu, P., Mu, M., Wang, R., Yang, P., Wang, X., Hu, C., Ping, R., Hu, P., Li, T., Cao, F., Chang, C., Hu, Q., Jin, Y., & Xu, G. (2020). Clinical features of 85 fatal cases of COVID-19 from Wuhan. A retrospective observational study. American Journal of Respiratory and Critical Care Medicine, 201(11), 1372-1379. https://doi.org/10.1164/rccm.202003-0543oc
COVID-19 Map - Johns Hopkins Coronavirus Resource Center. (n.d.). Johns Hopkins Coronavirus Resource Center. Accessed: Jul. 31, 2024. [Online]. Available: https://coronavirus.jhu.edu/map.html
Mahesh, B. (2020). Machine learning algorithms - A review. International Journal of Science and Research (IJSR), 9(1), 381-386. https://doi.org/10.21275/art20203995
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York: Springer. https://link.springer.com/book/9780387310732
Agatonovic-Kustrin, S., & Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis, 22(5), 717-727. https://doi.org/10.1016/s0731-7085(99)00272-1
Danang, A. P., Bakar, M. A., Ismail, N. B., & Mashuri, M. (2022). ANN-based methods for solving partial differential equations: A survey. Arab Journal of Basic and Applied Sciences, 29(1), 233-248. https://doi.org/10.1080/25765299.2022.2104224
Amran, S. N. Z., & Mohamed, N. (2021). Forecasting electricity supplied in Turkey using holt-winters’ multiplicative method and artificial neural network (ANN) models. Universiti Malaysia Terengganu Journal of Undergraduate Research, 3(3), 131-142. https://doi.org/10.46754/umtjur.v3i3.225
Idrus, N., & Mohamed, N. (2020). Forecasting the number of airplane passengers using boxjenkins and artificial neural network in Malaysia. Universiti Malaysia Terengganu Journal of Undergraduate Research, 2(4), 89-100. https://doi.org/10.46754/umtjur.v2i4.183
Bakar, M. A., Mohamed, N., Pratama, D. A., Yusran, M. F. A., Aleng, N. A., Yanuar, Z., & Niken, L. (2021). Modelling lock-down strictness for COVID-19 pandemic in ASEAN countries by using hybrid ARIMA-SVR and hybrid SEIR-ANN. Arab Journal of Basic and Applied Sciences, 28(1), 204-224. https://doi.org/10.1080/25765299.2021.1902606
Qu, Y., & Zhao, X. (2019). Application of LSTM neural network in forecasting foreign exchange price. Journal of Physics: Conference Series, 1237(4), Article 042036. https://doi.org/10.1088/1742-6596/1237/4/042036
Zeroual, A., Harrou, F., Dairi, A., & Sun, Y. (2020). Deep learning methods for forecasting COVID-19 time-series data: A comparative study. Chaos Solitons & Fractals, 140, 110121. https://doi.org/10.1016/j.chaos.2020.110121
Awad, M., & Khanna, R. (2015). Support vector regression. In Efficient Learning Machines (pp. 67-80). Apress. https://doi.org/10.1007/978-1-4302-5990-9_4
Amirkhalili, Y. S., Aghsami, A., & Jolai, F. (2020). Comparison of time series ARIMA model and support vector regression. International Journal of Hybrid Information Technology, 13(1), 7-18. https://doi.org/10.21742/ijhit.2020.13.1.02
Nava, N., Di Matteo, T., & Aste, T. (2018). Financial Time series forecasting using empirical mode decomposition and support vector regression. Risks, 6(1), Article 7. https://doi.org/10.3390/risks6010007
Vapnik, V. N. (2013). The nature of statistical learning theory (2nd ed., pp. 314). New York: Springer. Accessed: Jul. 31, 2024. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=EqgACAAAQBAJ&oi=fnd&pg=PR7&dq=Vapnik+V+(1995)+The+nature+of+statistical+learning+theory,+2nd+edn.+Springer,+New+York&ots=g5F-jv4Z94&sig=HH1U1C8Q775NuBNU4j7Km8HC4F0
Gers, F., Schmidhuber, J., & Cummins, F. (1999). Learning to forget: continual prediction with LSTM. 1999 Ninth International Conference on Artificial Neural Networks ICANN 99. (Conf. Publ. No. 470), Edinburgh, UK, 1999, pp. 850-855. Available: https://ieeexplore.ieee.org/abstract/document/6789445/
Deng, C., Huang, G., Xu, J., & Tang, J. (2015). Extreme learning machines: New trends and applications. Science China Information Sciences, 58(2), 1-16. https://doi.org/10.1007/s11432-014-5269-3
Long, N., Gianola, D., Rosa, G. J. M., & Weigel, K. A. (2011). Application of support vector regression to genome-assisted prediction of quantitative traits. Theoretical and Applied Genetics, 123(7), 1065-1074. https://doi.org/10.1007/s00122-011-1648-y
Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural Computation, 12(10), 2451-2471. https://doi.org/10.1162/089976600300015015
Smagulova, K., & James, A. P. (2019). A survey on LSTM memristive neural network architectures and applications. The European Physical Journal Special Topics, 228(10), 2313-2324. https://doi.org/10.1140/epjst/e2019-900046-x
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Journal of Mathematical Sciences and Informatics
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
