Networked prediction technology for dangerous factors of vascular cognitive impairment

Abstract

The invention discloses a networked prediction technology for the dangerous factors of vascular cognitive impairment. The method comprises the steps of dangerous factor collection and evaluation. A vascular cognitive impairment dangerous factor decision tree prediction model is established through a training set, and codes are compiled and visualized to establish a database. In order to store testrecords of subject data, necessary links for managing a large amount of data are conveniently carried out. Finally, a data result table with specific guiding significance is survived according to a designed calculation method. The network implementation is achieved, and a user can use the system anytime and anywhere through the IP address of the server and a wireless network approach. According to the invention, an APP for model generation and manufacture based on the networked algorithm is easier to operate, so that the APP is suitable for people to use. The accuracy of correlation evaluation of dangerous factors and cognitive impairment is high, and the good convenience and scientificity are provided for users.

Description

technical field [0001] The invention belongs to the field of medical technology, and relates to a networked prediction technology for risk factors of vascular cognitive impairment. Background technique [0002] Vascular cognitive impairment (VCI) refers to a large category of syndromes ranging from mild cognitive impairment to dementia caused by cerebrovascular diseases. Previous studies have believed that the influencing factors of VCI are mainly the influencing factors of cerebrovascular disease. At present, the influencing factors of VCI are mostly based on clinical experience judgment and traditional Logistic regression analysis. There is a lack of prediction models for the occurrence of VCI caused by various influencing factors, and there are no patients. Have a web-based assessment of those risk factors that can lead to cognitive impairment. By mining the demography, lifestyle factors and previous disease factors of patients with cerebrovascular disease, we found a wa...

AI Technical Summary

Problems solved by technology

Previous studies have believed that the influencing factors of VCI are mainly the influencing factors of cerebrovascular disease. At present, the influencing factors of VCI are mostly based on clinical experience judgment and traditional Logistic regression analysis. There is a lack of prediction models for the occurrence of VCI caused by various influencing factors, and there are no patients. Web-based assessment of risk factors for cognitive impairment

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