Abstract
The purpose is to minimize the substantial losses caused by public health emergencies to people’s health and daily life and the national economy. The tuberculosis data from June 2017 to 2019 in a city are collected. The Structural Equation Model (SEM) is constructed to determine the relationship between hidden and explicit variables by determining the relevant indicators and parameter estimation. The prediction model based on Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) is constructed. The method’s effectiveness is verified by comparing the prediction model’s loss value and accuracy in training and testing. Meanwhile, 50 pieces of actual cases are tested, and the warning level is determined according to the T-value. The results show that comparing and analyzing ANN, CNN, and the hybrid network of ANN and CNN, the hybrid network’s accuracy (95.1%) is higher than the other two algorithms, 89.1 and 90.1%. Also, the hybrid network has sound prediction effects and accuracy when predicting actual cases. Therefore, the early warning method based on ANN in deep learning has better performance in public health emergencies’ early warning, which is significant for improving early warning capabilities.
Highlights
With the continuous development of the economy, culture, and technology of all countries globally, people’s life quality is gradually improving
The results showed that the two proposed methods could perform better than algorithms such as support vector machines, random forests, and deep neural networks
The results indicated that the dangerous patient would have a cardiac arrest 24 h after the prediction, showing the prediction model’s excellent performances (Jang et al, 2020)
Summary
With the continuous development of the economy, culture, and technology of all countries globally, people’s life quality is gradually improving. The COVID-19 has spread throughout the world This major pandemic has caused severe threats to human health and impacts people’s daily lives, social development, the market economy, and national security (Piciullo et al, 2018; Shenfield et al, 2018; Syafrudin et al, 2019). The relevant news and other information data are intelligently extracted and analyzed to predict the early warning level of events, assisting relevant departments to improve the emergency detection efficiency. It guarantees the follow-up management and coordination work, thereby reducing all kinds of losses caused by public health emergencies to society
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