Prediction method of high blood pressure based on incremental neural network model and prediction system

Abstract

The invention discloses a prediction method of high blood pressure based on incremental neural network model. The method comprises the steps that a database of daily data of high blood pressures is built; a neural network model is trained; daily living data are collected and sent to a server, and are saved to a daily data sheet of a user; daily data are extracted from the daily data sheet of the user, and form an N dimensional vector, after the normalization is done, the data are input in a hypertensive pathological neural network model to conduct probabilistic prediction of the hazard level of high blood pressure; the value W of the hazard level of high blood pressure is judged whether or not being larger than or equal to 3 using a smart home high blood pressure care equipment; when a user receives a warning from an annunciator, the user goes to the hospital to check, the check results are sent back to the server through the smart home high blood pressure care equipment, the servers judges whether or not the check results are correct; an incremental calculation method is carried out when the check results are incorrect, and the neural network model is dynamically corrected. The method and system are accurate in prediction, and the neural network model is tailor made for every user.

Description

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

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 high blood pressure accurately

[0003] Previously, most of the high blood pressure 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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