Hepatitis B prediction method and prediction system based on incremental neural network model

Abstract

The invention discloses a hepatitis B prediction method based on an incremental neural network model. The hyperthyroidism prediction method comprises following steps that a database of hepatitis B daily data is established; a neural network model is trained; daily life data is acquired and sent to a server, and is saved to a user daily data recording chart; intraday data is extracted from the user daily data recording chart to form an n-dimensional vector, after normalization processing, the n-dimensional vector is input into a hepatitis B pathology neural network model to carry out hepatitis B probability prediction; whether the hepatitis B probability value is larger than 0.5 or not is determined by an intelligent household hepatitis B nursing device; when that the user suffers from the hepatitis B is determined, the user goes to the hospital for check-up himself, and sends the check-up result back to the server through the intelligent household hepatitis B nursing device, and the server determines whether the check-up result is correct or not; when the check-up result is wrong, an incremental algorithm is executed, and the neural network model is dynamically corrected. The hepatitis B prediction method based on the incremental neural network model 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 method and system for predicting hepatitis B based on an incremental neural network model. Background technique [0002] At present, all health management systems in China have set up hepatitis B 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 judge ...

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 individual's hepatitis B

[0003] Previously, most of the hepatitis B 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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