Incremental neural network model based urticaria prediction method and system

Abstract

The invention discloses an incremental neural network model based urticaria prediction method. The method includes the following steps: setting up a daily urticaria data database; training a neural network model; acquiring daily life data prior to sending to a server, and storing the daily life data into a daily user data log sheet; extracting current day data from the daily user data log sheet, forming n-dimensional vectors, and performing normalization processing prior to inputting the data into a urticaria pathology neural network model for urticaria probability prediction; smart household urticaria nursing equipment judging whether a urticaria probability value is larger than 0.5 or not; when a user is judged to have urticaria, the user going to hospital for examination by himself, transmitting examination results to the server through the smart household urticaria nursing equipment, and the server judging whether the examination results are accurate or not; when the examination results are wrong, executing an incremental algorithm, and subjecting the neural network model to dynamic correction. Predication is accurate, 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 an urticaria 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 urticaria 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 ju...

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 an individual's hives accurately

[0003] Previously, most of the urticaria predictions used the BP neural network model, but when new detection data is generated, the neural network model must be retrained, 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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