KR-102960327-B1 - Preprocessing method of biocomponent measurement data for growth prediction

Abstract

The present invention relates to a method, apparatus, and computer program for preprocessing input data for predicting the growth of a child or adolescent. A method for preprocessing biocomponent measurement data for growth prediction according to an exemplary embodiment of the present invention may include the steps of receiving body data of a subject, generating a first variable based on the body data of the subject, and determining an error in the body data by comparing the first variable with a preset value.

Inventors

• 성제혁
• 김지훈
• 천도현
• 강종호

Assignees

• 주식회사 지피

Dates

Publication Date

20260508

Application Date

20230830

Claims (15)

1. In a method for preprocessing biocomponent measurement data for growth prediction performed by a computing device, Step of receiving the subject's biocomponent data; A step of receiving identification data of the subject independently of the above biological component data; A step of generating first data by linking the above biocomponent data and the above identification data; A data transmission error determination step in which, when a preset value is measured in the first data, the age of the subject entered through the identification data is compared with a preset reference value, and if the age of the subject exceeds the preset reference value, the item in the first data where the preset value was measured is deleted, and if the age of the subject is less than or equal to the preset value, all items measured on the measurement date including the item in the first data where the preset value was measured are deleted; After the above-mentioned data transmission error determination step, a step of generating a first variable as the sum of body fat mass, soft lean mass, and osseous mineral among the subject's biocomposition data; and A data measurement error determination step for determining the biocomponent data as an error when the difference rate is greater than or equal to a preset threshold value when comparing the first variable with the subject's weight; A method for preprocessing biocomponent measurement data for growth prediction, characterized in that the previously set reference value in the data measurement error judgment step is set differently according to the subject's age in months.
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8. In paragraph 1, The above data measurement error determination step is, A method for preprocessing biocomponent measurement data for growth prediction, further comprising the step of determining whether the age of the subject in months among the identification data corresponds to a preset standard before comparing the first variable with any one of the values of the biocomponent data.
9. In paragraph 8, A method for preprocessing biocomponent measurement data for growth prediction, further comprising the step of deleting the biocomponent data when the result of comparing the value of either the first variable or the biocomponent data falls within a preset range.
10. In paragraph 8, A method for preprocessing biocomponent measurement data for growth prediction, further comprising the step of generating second data when the result of comparing the value of either the first variable or the biocomponent data does not fall within a preset range.
11. A program stored on a computer-readable recording medium comprising program code for executing a method for preprocessing biocomponent measurement data for growth prediction as described in any one of claims 1, 8 to 10.
12. A computer-readable recording medium having a program recorded thereon for executing a method for preprocessing biocomponent measurement data for growth prediction as described in any one of claims 1, 8 to 10.
13. In a preprocessing device for biocomponent measurement data for growth prediction, A first input unit for receiving the subject's biocomponent data; A second input unit for receiving identification data of the above subject; A connection unit that generates first data by connecting data received through the first input unit and the second input unit; An error detection unit that, when a preset value is measured in the first data, compares the age of the subject entered through the identification data with a preset reference value, deletes the item in the first data where the preset value was measured if the age of the subject exceeds the preset reference value, and deletes all items measured on the measurement date that include the item in the first data where the preset value was measured if the age of the subject is less than or equal to the preset value; A variable generation unit that generates a first variable from the sum of the body fat mass, soft lean mass, and osseous mineral mass among the body data of the subject; and It includes an error determination unit that determines the biocomponent data as an error when the difference rate is greater than or equal to a preset threshold value by comparing the first variable with the subject's weight. A preprocessing device for biocomponent measurement data for growth prediction, characterized in that the previously set reference value in the error judgment unit is set differently according to the subject's age in months.
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Description

Preprocessing method of biocomponent measurement data for growth prediction The present invention relates to a method, apparatus, and computer program for preprocessing input data for predicting the growth of children or adolescents. With the recent advancement of artificial intelligence technology, AI technologies are being applied across various fields, and methods are being developed and used to generate additional information by extracting inherent features from data through neural network models, replacing conventional data processing methods. Recently, artificial intelligence technology has moved beyond simply tracking and detecting objects to being applied to learn past history and derive current features that reflect future predictions or time-series changes. Furthermore, in data-learning artificial intelligence, data quality is known to be one of the key factors in AI performance. For example, leading companies possessing the most advanced AI-based autonomous driving technologies emphasize that securing high-quality data is crucial. Among these, predictive analytics is a technology within the fields of statistics and data mining that extracts information from data and uses it to predict trends and behavioral patterns. Such predictive analytics can be applied to all areas requiring decision-making based on information obtained from data. The core of predictive analytics lies in predicting unknown variables after understanding the relationships between variables. To this end, various approaches are being used depending on the data characteristics and the prediction target. Among the various fields requiring predictive analysis, there is the field of physical growth in children and adolescents. There is significant interest among parents and adolescents regarding when height growth will occur and how much they will grow. Conventional methods for predicting height growth involve predicting growth plates by X-ray imaging or analyzing the relationship with genetic and environmental factors (Registered Patent Publication No. 10-2075743, Registered Patent Publication No. 10-1866208), or they also propose methods for converting physical data from sample subjects with different measurement times or frequencies into a format suitable for training a growth prediction model (Registered Patent Publication No. 10-2198302). Since children and adolescents have growth stages with distinct characteristics, taking this into account can enhance the reliability of providing solutions based on predicted data and analysis. In particular, since children or adolescents are in a period of rapid physical change depending on their growth stage, it is necessary to first identify errors in input (or measurement) data (biocomposition data) to secure high-quality data that can improve the accuracy of growth prediction and the performance of AI learning. FIG. 1 is a diagram showing the structure of a growth prediction system according to an exemplary embodiment of the present invention. FIG. 2 is a diagram showing the configuration of a growth prediction and solution generation device according to an exemplary embodiment of the present invention. Figure 3 is a diagram showing the data preprocessing unit of Figure 2. FIG. 4 is a diagram showing predicted elongation and target elongation at each growth stage according to an exemplary embodiment of the present invention. FIG. 5 is a diagram showing growth in obesity and normal weight at different growth stages of a male child according to an exemplary embodiment of the present invention. FIG. 6 is a diagram showing growth in obesity and normal weight at different growth stages of a girl according to an exemplary embodiment of the present invention. FIG. 7 is a diagram showing the configuration of a neural network that performs growth prediction and solution provision according to an exemplary embodiment of the present invention. FIG. 8 is a diagram showing a first neural network model according to an exemplary embodiment of the present invention. FIG. 9 is a diagram showing a second neural network model according to an exemplary embodiment of the present invention. FIGS. 10 to 12 are drawings illustrating a method for preprocessing biocomponent measurement data for growth prediction according to an exemplary embodiment of the present invention. FIG. 13 is a diagram showing biocomponent data of a subject according to an exemplary embodiment of the present invention. FIG. 14 is a diagram showing data connecting the biocomponent data and identification data of a subject according to an exemplary embodiment of the present invention. Hereinafter, specific embodiments of the present invention will be described with reference to the drawings. The following detailed description is provided to facilitate a comprehensive understanding of the methods, apparatuses, and/or systems described herein. However, this is merely illustrative and the present invention is not limited thereto. In describing