Colonitis prediction method and colonitis prediction system based on incremental nerve network model

Abstract

The invention discloses a colonitis prediction method based on an incremental nerve network model. The colonitis prediction method comprises the following steps of: establishing a colonitis daily database; training the nerve network model; acquiring daily life data, transmitting the daily life data to a server, and storing the daily life data into a user daily data record sheet; extracting data on that day from the user daily data record sheet, forming n-dimensional vector, carrying out normalization processing, inputting the processed data into the colonitis pathology nerve network model, and carrying out colonitis probabilistic prediction; judging whether a colonitis probabilistic value of intelligent family colonitis nursing equipment is more than 0.5; when the condition that a user suffers from colonitis is determined, carrying out the step that the user goes to the hospital to be inspected, and transferring the inspected result to the server through the intelligent family colonitis nursing equipment, and judging whether the inspected result is correct or not through the server; and when the inspected result is incorrect, executing incremental algorithm, and carrying out dynamic correction on the nerve network model. According to the colonitis prediction method, the prediction is accurate, and the nerve network model is customized according to each user.

Description

technical field [0001] The invention belongs to the field of medical technology, in particular to a colitis prediction method and a prediction system based on an incremental neural network model. Background technique [0002] At present, all health management systems in China have set up colitis 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 colitis accurately

[0003] Previously, most colitis 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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