The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Coronavirus disease (COVID-19) cases analysis using machine-learning applications
Open accessAbstract
Today world thinks about coronavirus disease that which means all even this pandemic disease is not unique. The purpose of this study is to detect the role of machine-learning applications and algorithms in investigating and various purposes that deals with COVID-19. Review of the studies that had been published during 2020 and were related to this topic by seeking in Science Direct, Springer, Hindawi, and MDPI using COVID-19, machine learning, supervised learning, and unsupervised learning as keywords. The total articles obtained were 16,306 overall but after limitation; only 14 researches of these articles were included in this study. Our findings show that machine learning can produce an important role in COVID-19 investigations, prediction, and discrimination. In conclusion, machine learning can be involved in the health provider programs and plans to assess and triage the COVID-19 cases. Supervised learning showed better results than other Unsupervised learning algorithms by having 92.9% testing accuracy. In the future recurrent supervised learning can be utilized for superior accuracy.
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Cites
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Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
A new coronavirus associated with human respiratory disease in China
Wu, F., Zhao, S., Yu, B., Chen, Y.-M., Wang, W., Song, Z.-G., Hu, Y., Tao, Z.-W., Tian, J.-H., Pei, Y.-Y., et al. (2020) · Nature · doi:10.1038/s41586-020-2008-3 · Cited by 11,688
Pattern Recognition and Machine Learning
(2006) · doi:10.1007/978-0-387-45528-0 · Cited by 9,824
Artificial intelligence and machine learning to fight COVID-19
Alimadadi, A., Aryal, S., Manandhar, I., Munroe, P. B., Joe, B., & Cheng, X. (2020) · Physiological Genomics · doi:10.1152/physiolgenomics.00029.2020 · Cited by 608
Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment
Jamshidi, M., Lalbakhsh, A., Talla, J., Peroutka, Z., Hadjilooei, F., Lalbakhsh, P., Jamshidi, M., Spada, L. L., Mirmozafari, M., Dehghani, M., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3001973 · Cited by 551
Covid-19: identifying and isolating asymptomatic people helped eliminate virus in Italian village
Day, M. (2020) · BMJ · doi:10.1136/bmj.m1165 · Cited by 409
Kassania, S. H., Kassanib, P. H., Wesolowskic, M. J., Schneidera, K. A., & Detersa, R. (2021) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2021.05.013 · Cited by 375
Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study
Brinati, D., Campagner, A., Ferrari, D., Locatelli, M., Banfi, G., & Cabitza, F. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01597-4 · Cited by 362
Machine learning based approaches for detecting COVID-19 using clinical text data
Khanday, A. M. U. D., Rabani, S. T., Khan, Q. R., Rouf, N., & Mohi Ud Din, M. (2020) · International Journal of Information Technology · doi:10.1007/s41870-020-00495-9 · Cited by 331
Cited by
Machine learning and deep learning approach for medical image analysis: diagnosis to detection
Rana, M., & Bhushan, M. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14305-w · Cited by 365
Metadata
- Authors
- Kwekha-Rashid, A. S., Abduljabbar, H. N., & Alhayani, B.
- Venue
- Applied Nanoscience
- Published
- 2021-05-21
- Volume
- 13
- Issue
- 3
- Pages
- 2013-2025
- ISSN
- 2190-5509 (ISSN-L)
- 2190-5517
- OpenAlex
- W3165437720
- Cited by
- 348
- References
- 38
- FWCI
- 37.90
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Kwekha-Rashid, A. S., Abduljabbar, H. N., & Alhayani, B. (2023). Coronavirus disease (COVID-19) cases analysis using machine-learning applications. Applied Nanoscience. https://doi.org/10.1007/s13204-021-01868-7
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
