Abstract
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.
Highlights
According to the statistics of the coronavirus disease 2019 (COVID-19) pandemic reported by the World Health Organization (WHO), there are already more than 56 million confirmed cases and 1.35 million deaths as of 15 : 59 Central European Time (CET),November 20, 2020, which indicate a very serious global epidemic situation. e number of COVID-19-infected patients has exceeded 1 million in many countries, including the United States, India, Brazil, and France. e United States, in particular, has over 10 million confirmed cases of COVID-19. us, it is critical to conduct a status analysis and research on the effects of epidemic prevention and control measures based on different epidemic situations in countries worldwide
To complete the task of epidemic prediction, we developed a novel prediction method based on artificial intelligence (AI) and the Internet of ings
(4) e simulation results show that the predictions made by the AI designed in this paper’s random forest model are more accurate than those made by the logistic regression and support vector machine (SVM) algorithms
Summary
According to the statistics of the coronavirus disease 2019 (COVID-19) pandemic reported by the World Health Organization (WHO), there are already more than 56 million confirmed cases and 1.35 million deaths as of 15 : 59 Central European Time (CET),November 20, 2020, which indicate a very serious global epidemic situation. e number of COVID-19-infected patients has exceeded 1 million in many countries, including the United States, India, Brazil, and France. e United States, in particular, has over 10 million confirmed cases of COVID-19. us, it is critical to conduct a status analysis and research on the effects of epidemic prevention and control measures based on different epidemic situations in countries worldwide. Mathematical models are often used by researchers to derive the nonspreading conditions of infectious diseases and predict and analyze the trends of epidemics and infected populations. One of the currently used epidemic prediction models is the Malthusian growth model[1]. This model still has a long way to go before being applied in the real world. To complete the task of epidemic prediction, we developed a novel prediction method based on artificial intelligence (AI) and the Internet of ings. On account of the existing model, many Internet machine learning algorithms can be employed for the prediction method. We investigated a research-related work and analyzed the common algorithms used in medical prediction.
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