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Human body composition prediction method based on AIC and improved entropy weight method

A prediction method and body composition technology, applied in the field of bioinformatics, can solve problems such as high dimensionality, nonlinearity, and small samples, and achieve the effects of improving prediction accuracy, simplifying fitting models, and effective detection means

Active Publication Date: 2017-03-08
DALIAN UNIVERSITY
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Problems solved by technology

There are also highly nonlinear and serious correlations between these physiological parameters, and the existing human body composition models are difficult to meet this need
[0003] With the continuous advancement of medical measurement technology, the measurable physiological characteristics have developed on a large scale, and present the characteristics of few samples and high dimensionality, which brings great challenges to the processing and analysis of traditional physiological data, among which redundant features The existence of the indirect aggravated the adverse effects, resulting in insufficient prediction of human body composition

Method used

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  • Human body composition prediction method based on AIC and improved entropy weight method
  • Human body composition prediction method based on AIC and improved entropy weight method
  • Human body composition prediction method based on AIC and improved entropy weight method

Examples

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Embodiment 1

[0076] This embodiment provides a human body composition prediction method based on AIC and improved entropy weight method, including:

[0077] S1: Considering the differences in various parts of the human body, select a five-segment impedance model, collect data and construct an original feature set F of physiological information samples;

[0078] S2: Consider a series of other physiological parameters that affect body composition, add the original feature set F of the physiological information sample, and construct the first feature parameter and the second feature parameter;

[0079] At present, the five-segment human body impedance model is the most commonly used segmented impedance model, which takes into account the differences of various parts of the human body, and divides the human body into five segments of impedance: right upper limb, left upper limb, trunk, right lower limb, and left lower limb. In addition to considering the five-segment impedance value R in the m...

Embodiment 2

[0091] As a supplement to Example 1, the steps for solving the human body composition fitting model are as follows:

[0092] S51: Suppose the evaluation event has m objects, n parameters, x ij is the j-th indicator under the i-th object, then the decision matrix Y with m rows and n columns = {x ij} m×n Calculated according to the bigger the better index:

[0093]

[0094] Or the smaller the better index calculation:

[0095]

[0096] S52: Eliminate the different dimension units of different indicators of the object to form a unified matrix: In order to make ln(Y′ ij ) is meaningful, generally it can be assumed that: when Y′ ij =0, Y' ij ln(Y′ ij )=0. But Y' ij =1, ln(Y' ij ) is also equal to 0, which is obviously inconsistent with the reality and contrary to the meaning of entropy. Therefore, for Y′ ij to modify:

[0097] S53: Calculate the entropy value e in the formula j is the entropy value corresponding to the jth evaluation indicator; if Y′ ij = 0...

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Abstract

The invention discloses a human body composition prediction method based on AIC and improved entropy weight method. The method comprises the following steps: S1, selecting an impedance model, collecting data and constructing an original feature set F of a physiological information sample; S2, adding the original feature set F of the physiological information sample to construct a first feature parameter and a second feature parameter; S3, selecting an AIC stabilization model by use of an akaike information criterion; S4, computing the value of an AIC, and selecting a feature combination with the minimum AIC value to obtain a feature parameter matrix, analyzing the influence of each feature parameter to a fitting model, and modifying the feature parameter matrix; S5, introducing the information entropy to obtain a unified matrix, computing the entropy and the weight; and S6, solving a feature parameter matrix coefficient to obtain a human body composition fitting model. The established human body composition prediction model can improve the human body composition prediction precision and provides more effective detection means for the human body composition research and clinical application.

Description

technical field [0001] The invention belongs to the field of bioinformatics, in particular to a human body composition prediction method based on AIC and improved entropy weight method. Background technique [0002] Changes in body composition reflect changes in physical health to a certain extent. Accurate prediction of body composition is of great significance to the regulation of human nutritional status and the prevention of diseases. There are many parameters that affect body composition, mainly including physiological electrical impedance parameters and general physiological characteristic parameters. There are also highly nonlinear and serious correlations between these physiological parameters, and the existing human body composition models are difficult to meet this need. [0003] With the continuous advancement of medical measurement technology, the measurable physiological characteristics have developed on a large scale, and present the characteristics of few sam...

Claims

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Application Information

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IPC IPC(8): G06F19/00
CPCG16H50/20G16H50/30
Inventor 陈波郑庆国白旭飞俞洁吴金峰朱康特
Owner DALIAN UNIVERSITY
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