Deep Learning-based Prediction Model for Risk Factor of Atherosclerotic Cardiovascular Disease

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 project 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 Recurrent Neural Network (RNN) and
Long Short-Term Memory (LSTM). 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.