Battling COVID-19 using machine learning: A review

被引:20
|
作者
Chadaga, Krishnaraj [1 ]
Prabhu, Srikanth [2 ]
Vivekananda, Bhat K. [2 ]
Niranjana, S. [2 ]
Umakanth, Shashikiran [3 ]
机构
[1] Manipal Acad Higher Educ, Dept Comp Sci Engn, Manipal, Karnataka, India
[2] Manipal Acad Higher Educ, Dept Biomed Engn, Manipal, Karnataka, India
[3] Manipal Acad Higher Educ, Dr TMA Pai Hosp, Dept Med, Manipal, Karnataka, India
来源
COGENT ENGINEERING | 2021年 / 8卷 / 01期
关键词
SARS-CoV-2; COVID-19; CT-Scans; X-ray; sound analysis; blood tests; drug development; vaccine development; machine learning; ARTIFICIAL-INTELLIGENCE; NEURAL-NETWORK; AI; PREDICTION; CLASSIFICATION; SYSTEM; MODEL; RISK; IOT; 5G;
D O I
10.1080/23311916.2021.1958666
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2) known as Coronavirus surfaced in late 2019. It turned out to be a life-threatening disease and is causing chaos all around the world. The World Health Organisation (WHO) declared it a pandemic in March 2020. To handle COVID-19 related problems, research in many areas of science was introduced. Machine learning (ML), being one of the most successful stories in recent times is widely used to solve a variety of problems in our everyday life. Here, an overview of machine learning that tackles the pandemic is discussed in the beginning. Various datasets related to COVID-19 are also explored. Diagnosis of this viral disease using CT-Scans, X-ray images, sound analysis and blood tests using machine learning are presented in-depth. Drug and vaccine development using machine learning for COVID-19 are also discussed. Pandemic management and control were also examined. The main objective of this paper is to conduct a systematic review of machine learning applications that fight the deadly virus. This paper helps the researchers to understand and analyse the data trends related to COVID-19 and also prepare for a future outbreak which might happen due to new strains of COVID-19. Challenges and directions for the future are also provided.
引用
收藏
页数:41
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