A Novel Prediction Method Based on Artificial Intelligence and Internet of Things for Detecting Coronavirus Disease (COVID-19)

Novel coronavirus spreads fast and has a huge impact on the whole world. In light of the spread of novel coronaviruses, we develop one big data prediction model of novel coronavirus epidemic in the context of intelligent medical treatment, taking into account all factors of infection and death and i...

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Bibliographic Details
Published in:Security and communication networks 2021-11, Vol.2021, p.1-11
Main Authors: Jing, Shenqi, Qian, Qijie, She, Hao, Shan, Tao, Lu, Shan, Guo, Yongan, Liu, Yun
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Big Data
Cleaning
Coronaviruses
Coupling
COVID-19
Data collection
Epidemics
Health services
Internet of Things
Machine learning
Medical research
New technology
Performance prediction
Prediction models
Training
Trends
Viral diseases
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Summary:Novel coronavirus spreads fast and has a huge impact on the whole world. In light of the spread of novel coronaviruses, we develop one big data prediction model of novel coronavirus epidemic in the context of intelligent medical treatment, taking into account all factors of infection and death and implementing emerging technologies, such as the Internet of Things (IoT) and machine learning. Based on the different application characteristics of various machine learning algorithms in the medical field, we propose one artificial intelligence prediction model based on random forest. Considering the loose coupling between the data preparation stage and the model training stage, such as data collection and data cleaning in the early stage, we adopt the IoT platform technology to integrate the data collection, data cleaning, machine learning training model, and front- and back-end frameworks to ensure the tight coupling of each module. To validate the proposed prediction model, we perform the evaluation work. In addition, the performance of the prediction model is analyzed to ensure the information accuracy of the prediction platform.
ISSN:1939-0114
1939-0122
DOI:10.1155/2021/7812223