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

Abstract

The invention discloses a hyperlipidemia prediction method based on an incremental neural network model. The hyperlipidemia prediction method comprises the following steps: establishing a database of daily data of hyperlipidemia; training a neural network model; acquiring daily living data and transmitting to a server; extracting data of a day from a daily data record table of a user, forming an n-dimensional vector, performing normalization processing, inputting into a hyperlipidemia pathology neural network model, and performing hyperlipidemia criticality probability prediction; determining whether a hyperlipidemia criticality value W is greater than or equal to 3 or not by using intelligent domestic hyperlipidemia nursing equipment; when the user receives an alert of an alarm, reminding the user to take inspection in a hospital, transmitting an inspection result to the server, and determining whether the inspection result is correct or not by the server; when the inspection result is wrong, implementing an incremental algorithm, and performing dynamic modification on the neural network model. The hyperlipidemia prediction method is accurate in prediction, and the neural network model can be 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 hyperlipidemia based on an incremental neural network model. Background technique [0002] At present, all health management systems in China have set up the prediction and evaluation of hyperlipidemia, 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, una...

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 hyperlipidemia

[0003] Previously, most of the hyperlipidemia 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

Images