Abstract
To reduce the incidence of cerebrovascular disease and mortality, identifying the risks of cerebrovascular disease in advance and taking certain preventive measures are significant. This article was aimed to investigate the risk factors of cerebrovascular disease (CVD) in the primary prevention, and to build an early warning model based on the existing technology. The authors use the information entropy algorithm of rough set theory to establish the index system suitable for early warning model. Then, using the limited Boltzmann machine and direction propagation algorithm, the depth trust network is established by building and stacking RBM, and the back propagation is used to fine-tune the parameters of the network at the top layer. Compared with the LM-BP early-warning model, the deep confidence network model is more effective than traditional artificial neural network, which can help to identify the risk of cerebrovascular disease in advance and promote the primary prevention.
Highlights
Cerebrovascular disease (CVD) has the characteristics of high mortality and high morbidity
The aim of this study is to investigate the risk factors of cerebrovascular disease (CVD) in the primary prevention of CVD, and build an early warning model based on the existing technology
This article focuses on the early warning in the process of primary prevention to assess the risk of CVD accurately and timely by constructing the risk early warning model
Summary
Cerebrovascular disease (CVD) has the characteristics of high mortality and high morbidity. In advance to identify the risk of CVD, taking certain preventive and control measures can reduce the incidence and mortality of CVD. The risk recognition and prevention measures of CVD are still the world’s major medical challenges It is an urgent and important task to establish a practical and scientific early warning system to capture indicators of CVD crisis and formulate reasonable intervention measures at the present. The academic research of early warning for CVD risk is one of the hotspots in the field of CVD disease prevention and control at home and abroad, which has transferred from the risk factors of disease to auxiliary diagnosis, to the risk early warning
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