Abstract
With the development of medical digitization technology, artificial intelligence and big data technology, the medical model is gradually changing from treatment-oriented to prevention-oriented. In recent years, with the rise of artificial neural networks, especially deep learning, great achievements have been made in realizing image classification, natural language processing, text processing and other fields. Combining artificial intelligence and big data technology for disease risk prediction is a research focus in the field of intelligent medicine. Blood lipids are the main risk factors of cardiovascular and cerebrovascular diseases. If early prediction of abnormal blood lipids in iron and steel workers can be carried out, early intervention can be carried out, which is beneficial to protect the health of iron and steel workers. This paper around the steel workers dyslipidemia prediction problem for further study, firstly analyzes the influence factors of the steel workers dyslipidemia, discusses the commonly used method for prediction of disease, and then studied deep learning related theory, this paper introduces the two deep learning algorithms of RNN (Recurrent Neural Network) and LSTM (Long Short-Term Memory). Use the basic principle of Python language and the TensorFlow deep learning framework, establishes a prediction model based on two deep learning networks, and makes an example analysis. Experimental results show the LSTM prediction effect is superior to traditional RNN network, It provides scientific basis for the prevention of iron and steel dyslipidemia.
Highlights
As the pillar industry of the secondary industry, iron and steel industry has made an indelible contribution in the period of China from agricultural economy to industrial economy
The statistical results are shown in the table1: From the table 1, it can be seen that the rate of dyslipidemia in workers exposed to high temperature, high noise and long-term shift work is significantly higher than that of other workers, and the age, height, weight, length of service, marital status, education level, economic level, alcohol consumption, smoking and other factors of workers have a certain impact on the blood lipid
In this paper, a risk prediction model for dyslipidemia in steel workers based on RNN and LSTM networks was established
Summary
As the pillar industry of the secondary industry, iron and steel industry has made an indelible contribution in the period of China from agricultural economy to industrial economy. S. Cui et al.: Research on Risk Prediction of Dyslipidemia in Steel Workers Based on RNN and LSTM Neural Network be introduced into the cerebral cortex and autonomic nervous center through hearing, triggering a series of reactions in the central nervous system. This article focuses on the prediction of blood lipid risk for steel workers. Disease prediction mainly uses the traditional machine learning method, which requires the establishment of prediction model for data. The essential characteristic of this kind of network is that there are both internal feedback connections and feedforward connections between processing units It is a feedback dynamic system, which reflects the dynamic characteristics of the process in the calculation process and has stronger dynamic behavior and computing capacity than the feedforward neural network. By using Python language and TensorFlow deep learning framework, a prediction model based on two kinds of deep learning networks is built, and an example is analyzed and studied to compare the prediction effects of the two models
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