Sub-health prediction method and prediction system based on incremental neural network model

Abstract

The invention discloses a sub-health prediction method based on an incremental neural network model. The sub-health prediction method comprises the following steps that a sub-health daily data database is established; the neural network model is trained; daily life data is acquired and sent to a server; current-day data is extracted from a daily data record sheet of a user to form n-dimensional vectors, normalization processing is performed, and then the vectors are input into the neural network model of sub-health pathology to perform sub-health danger level possibility prediction; an intelligent household sub-health nursing device judges whether a sub-health danger level value W is greater than 03 or not; when the user receives warning of a warning indicator, the user goes to a hospital by himself / herself for examination and transmits an examination result to the server, and the server judges whether the examination result is correct or not; when the examination result is wrong, an incremental algorithm is executed, and dynamic correction is conducted on the neural network model. The sub-health prediction method is accurate in prediction, and the neural network model is customized for each user.

Description

technical field [0001] The invention belongs to the field of medical technology, in particular to a sub-health prediction method and prediction system based on an incremental neural network model. Background technique [0002] At present, all health management systems in China have set up sub-health prediction and evaluation, and the prediction method used is data matching. The principle is to input personal life data into the system, and the system matches the fixed data to obtain the probability of disease. However, due to the complexity and unpredictability of the human body and diseases, the detection and signal expression of biological signals and information in the form of expression and change rules (self-change and changes after medical intervention), the obtained data and information Analysis, decision-making and many other aspects have very complex nonlinear connections. Therefore, the use of traditional data matching can only be blind data screening, unable to j...

AI Technical Summary

Problems solved by technology

However, due to the complexity and unpredictability of the human body and diseases, the detection and signal expression of biological signals and information in the form of expression and change law (self-change and change after medical intervention), the acquired data and information There are very complex nonlinear relationships in analysis, decision-making and many other aspects

Therefore, the use of traditional data matching can only be blind data screening, unable to judge the logical relationship between data and variables, and the obtained value range deviation is large, resulting in very poor specificity of system prediction, so the current domestic health management The system cannot effectively predict the sub-health of individuals

[0003] Previously, most sub-health predictions used the BP neural network model, but when new detection data is generated, the neural network model must be trained again, and the calculation efficiency is extremely low

And when the scale of system users increases, the server will not be able to complete the training tasks in time

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