Cold prediction method and system based on incremental neutral network model

Abstract

The invention discloses a cold prediction method based on an incremental neutral network model. The method includes the following steps that a cold daily data database is established; the neutral network model is trained; daily life data is acquired and sent to a server, and the daily life data is saved in a user daily data record sheet; current-day data is extracted from the user daily data record sheet, n-directional vectors are formed and normalized, and the n-directional vectors are input in the cold pathology neutral network model for cold probability prediction; intelligent household cold nursing equipment judges whether the cold probability value is larger than 0.5 or not; when it is judged that the user has a cold, the user goes to a hospital for inspection by himself / herself, the inspection result is sent back to the server through the intelligent household cold nursing equipment, and the server judges whether the detection result is correct or not; when the inspection result is wrong, an incremental algorithm is executed, and the neutral network model is dynamically corrected. Prediction is accurate, and one neural network model is customized for each user.

Description

Technical field [0001] The invention belongs to the field of medical technology, and particularly relates to a cold and cold prediction method and a prediction system based on an incremental neural network model. Background technique [0002] At present, all domestic health management systems have set up cold and cold 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 get the probability of illness. However, due to the complexity and unpredictability of the human body and disease, in terms of the manifestation and change of biological signals and information (self changes and changes after medical intervention), the detection and signal expression of them, the data and information obtained Analysis, decision-making, and many other aspects have very complex nonlinear relationships. Therefore, the use of traditional data matching can only be blind data sc...

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 cold and flu

[0003] Previously, most of the cold and cold predictions used the BP neural network model, but when new detection data was generated, the neural network model had to be trained again, and the calculation efficiency was 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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