Method for constructing health assessment iterative classifier model

A technology of health assessment and construction method, which is applied in the direction of health index calculation, neural learning method, biological neural network model, etc., can solve problems such as dependence on the degree of understanding, achieve good generalization ability, improve physical and mental health, and speed up the effect

Pending Publication Date: 2021-01-08
成都东方天呈智能科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The former conducts health surveys on the evaluators through a large number of questionnaires. The questions and answers in the questionnaires are designed by relevant experts to achieve the purpose of health assessment, but this method relies too much on the evaluators' understanding of their own health status, subjectivity stronger

Method used

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  • Method for constructing health assessment iterative classifier model
  • Method for constructing health assessment iterative classifier model
  • Method for constructing health assessment iterative classifier model

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Embodiment 1

[0041] A method for constructing a health assessment iterative classifier model, such as figure 1 , figure 2 As shown, collect health data and determine the numerical interval standard of each influencing factor, then divide the collected health data according to the numerical interval standard, and mark the categories; use the fuzzy set membership function to determine the degree of influence of each factor on the user's health, A training sample set required for training is formed; an iterative classifier model is trained, and the iterative classifier model adopts several weak classifiers to integrate a strong classifier, and the weak classifier adopts a BP neural network structure.

[0042] The iterative classifier adopts the integration of multiple weak classifiers to form a strong classifier, which is not easy to cause over-fitting in the training process. The BP neural network can have a certain fault-tolerant ability to unprocessed noise, and has good generalization a...

Embodiment 2

[0044] This embodiment is optimized on the basis of embodiment 1, such as figure 2 As shown, the fuzzy set membership function calculates the probability distribution of each data point through the Gaussian mixture model, and then assigns different membership degrees to different data points according to the obtained probability distribution. The invention proposes to establish a corresponding degree of membership for each factor affecting health, which can determine the degree of influence of each factor on health and speed up the speed of discovering existing problems.

[0045] At present, most researches on fuzzy membership function are based on the distance measurement membership degree constructed based on the distance from the sample point to the category center point. The closer the distance to the center point, the higher the membership degree, and its design directly affects the performance of the classifier. If there are discrete points in the data, the membership fun...

Embodiment 3

[0048] This embodiment is optimized on the basis of embodiment 1 or 2, as image 3 As shown, training an iterative classifier model mainly includes the following steps:

[0049] Step S100: Load the training sample set, initialize the weight of the classifier model, and train the weak classifier 1;

[0050] Step S200: Train the weak classifier according to the set number of iterations, and calculate the error rate e when the maximum number of iterations is reached t , update weight α t ;

[0051] Step S300: use the updated weight parameters to train the weak classifier 2;

[0052] Step S400: Repeat steps S100-S300 until all weak classifiers are trained;

[0053] Step S500: Using a combination strategy to integrate all weak classifiers into a strong classifier, and perform an accuracy test on the strong classifier, and select an optimal classification model.

[0054] Further, first calculate the average precision of each weak classification model after t iterations, and use...

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Abstract

The invention discloses a method for constructing a health assessment iterative classifier model, and the method comprises the following steps: collecting health data, determining a numerical value interval standard of each influence factor, dividing the collected health data according to the numerical value interval standard, and marking categories; determining the influence degree of each factoron the health of the user by adopting a fuzzy set membership function to form a training sample set required by training; training an iterative classifier model, adopting a plurality of weak classifiers in the iterative classifier model to integrate a strong classifier, and adopting a BP neural network structure in the weak classifier. According to the iterative classifier, a plurality of weak classifiers are integrated to form a strong classifier, so that an over-fitting condition is not easily caused in a training process. The BP neural network has a certain fault-tolerant capability for unprocessed noise, and has a good generalization capability. According to the invention, the health of the user can be fully analyzed, so the user can intuitively know the health condition of the user and discover the problem influencing the health in time.

Description

technical field [0001] The invention belongs to the technical field of health assessment, and in particular relates to a method for constructing a health assessment iterative classifier model. Background technique [0002] With the continuous development of the country's social economy, people have obtained better supplies in various aspects such as food, clothing, housing and transportation, and began to pursue better physical and mental health. In recent years, with the acceleration of the country's urbanization process, people's work and life pressures are also increasing. Many news broadcast negative news such as "a certain employee's sudden death" and "someone's depression". Through these information, we can learn about the impact of health on The importance of people, in order to prevent an irreversible situation, timely detection of health changes and the resolution of emerging hazards are the most important things. However, most of the existing health assessment met...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/30G06K9/62G06N3/08
CPCG16H50/30G06N3/084G06F18/214G06F18/24
Inventor 黄俊洁闫超杨凯
Owner 成都东方天呈智能科技有限公司
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