Hypertensive nephropathy prediction method and system based on incremental neural network model

Abstract

The invention discloses a hypertensive nephropathy prediction method based on an incremental neural network model, comprising the following steps of: establishing a daily data database of hypertensive nephropathy; training a neural network model; collecting and transmitting daily life data to a server; extracting the current data from the daily data record table of a user to form the n-dimensional vector, and then normalizing and inputting the data into the neural network model of hypertensive nephropathy for predicting risk probability of hypertensive nephropathy. Whether the risk degree W of hypertensive nephropathy is greater than or equal to 3 is judged by the intelligent household hypertensive nephropathy nursing instrument; the user is reminded of having physical examination in hospital when receiving a warning alert, the examination results are transmitted back to the server through the intelligent household nephropathy nursing instrument, the server determines whether the examination results are correct or not. When the examination results are incorrect, the incremental algorithm is implemented and the neural network model is modified dynamically. The hypertensive nephropathy prediction method predicts accurately, and the neural network model is tailored to each user.

Description

technical field [0001] The invention belongs to the field of medical technology, in particular to a method and system for predicting hypertensive nephropathy 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 hypertensive nephropathy, 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...

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 the individual's hypertensive nephropathy

[0003] Previously, most of the predictions of hypertensive nephropathy 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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