Amygdalitis prediction method based on incremental neural network model and prediction system

Abstract

The invention discloses an amygdalitis prediction method based on an incremental neural network model. The method includes the following steps that an amygdalitis daily data database is established; the neural network model is trained; daily life data is collected, sent to a server and stored in a user daily data record sheet; current day data is extracted from the user daily data record sheet to form an n-dimensional vector, and the n-dimensional vector is subjected to normalization processing and then input into the amygdalitis pathology neural network model for amygdalitis probability prediction; intelligent household amygdalitis care equipment judges whether the amygdalitis probability value is larger than 0.5 or not; when it is judged that a user suffers from amygdalitis, the user goes to hospital for an examination, the examination result is transmitted back to the server through the intelligent household amygdalitis care equipment, and the server judges whether the examination result is correct or not; when the examination result is wrong, an incremental algorithm is executed, and the neural network model is dynamically corrected. The 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 method and system for predicting tonsillitis based on an incremental neural network model. Background technique [0002] At present, all health management systems in China have set up tonsillitis 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 an individual's tonsillitis accurately

[0003] Previously, most of the tonsillitis 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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