Preprocessing method for predicting height measurement data for growth

Abstract

The present invention relates to a preprocessing method for input data for predicting growth of a child or a teenager, an apparatus, and a computer program, the preprocessing method for predicting height measurement data for growth according to an exemplary embodiment of the present invention comprising: a step of inputting time series biological component data for obtaining a subject; a step of inputting the identification data of the subject; extracting height data from the biological component data; a step of determining whether there is a reduced interval in the interval in which the height data is input; and a step of determining an error of the height data according to the growth stage including the reduced interval when there is an interval in which the height data is reduced.

Description

Technical Field

[0001] The present invention relates to a method, a device and a computer program for preprocessing input data for predicting the growth of children or adolescents. Background Art

[0002] Recently, with the development of artificial intelligence technology, artificial intelligence technology is applied to various fields, and methods of generating additional information by extracting features from data through neural network models are being developed and used to replace existing data processing methods.

[0003] Currently, artificial intelligence technology has gone beyond simple tracking and detection of objects and is also applied to learn from past experiences to predict the future or derive current features that reflect information about time series changes.

[0004] Furthermore, in AI that learns from data, the quality of data is considered one of the key factors for AI outcomes. For example, leading companies with the most advanced AI-based autonomous driving technologies have emphasized that ensuring high-quality data is key.

[0005] Among them, predictive analysis is a technology in the field of statistics and data mining that extracts information from data and uses it to predict trends, behavior patterns, etc. This predictive analysis can be applied to all fields where decisions need to be made based on information obtained from data. The core of predictive analysis is to predict unknown variables after understanding the relationship between variables.

[0006] To this end, various approaches are adopted depending on the data characteristics and predicted subjects.

[0007] Among the various fields that require predictive analysis is the physical growth of children or adolescents. Parents and adolescents are very interested in questions such as when and how much height they will grow.

[0008] In the past, a method has been proposed for predicting height or analyzing the relationship with genetic / environmental factors by photographing the growth plate through X-ray (Korean Patent Registration No. 10-2075743, Korean Patent Registration No. 10-1866208). In addition, a method has been proposed for preparing the physical data of sample subjects with different measurement periods or measurement times into a form suitable for learning a growth prediction model (Korean Patent Registration No. 10-2198302).

[0009] Since children or adolescents have their own unique growth stages, this needs to be taken into account to increase the reliability of solutions provided through predictive data and analysis.

[0010] Especially for children or adolescents, including a period of rapid physical changes depending on the growth stage, it is necessary to first perform a process to determine the errors in the input (or measured) data (biological component data) so that the accuracy of growth prediction and the results of artificial intelligence learning can be improved. Summary of the invention

[0011] Technical issues

[0012] Therefore, an object of the present invention is to solve the above-mentioned problems.

[0013] One of the various objects of the present invention is to provide a method, device and computer program for preprocessing input data to predict the growth of children or adolescents and provide customized solutions according to the growth stage.

[0014] Means of solving problems

[0015] In order to solve various embodiments of the subject of the present invention, a preprocessing method for height measurement data for predicting growth can be provided, which is a preprocessing method for height measurement data for predicting growth executed by a computing device, and includes: a step of inputting time series biological component data of a subject; a step of inputting identification data of the subject; a step of extracting height data from the biological component data; a step of judging whether there is a decreasing interval within the interval of the input height data; and when there is an interval of decreasing height data, a step of judging the error of the height data according to the growth stage containing the decreasing interval.

[0016] The present invention may further include a step of generating first data by connecting the time-series biological component data of the subject and the identification data of the subject.

[0017] The present invention may further include a step of deleting the biological component data if a preset value is measured in the biological component data after the first data is generated.

[0018] The present invention may be that the growth stages include a general growth stage, a rapid growth stage, a slow growth stage and a growth plate-free stage, and are classified according to the age in months of the identification data.

[0019] The present invention may be that, in the step of judging an error, when the reduction interval is included in the rapid growth stage, the height data of the reduction interval is deleted.

[0020] The present invention may be that the step of judging an error includes the step of calculating, when the reduction interval is included in the general growth stage, the difference between the height data of any first location and the height data of any second location forming the reduction interval and the average data values ​​of the corresponding months' ages of multiple sample subjects, respectively and independently.

[0021] In the present invention, the step of determining an error may further include a step of comparing a difference value calculated at the first location with a difference value calculated at the second location.

[0022] The present invention may be that, in the step of determining an error, if the difference calculated at the first location is smaller than the difference calculated at the second location, the height data at the second location is deleted.

[0023] The present invention may be that, in the step of determining an error, if the difference calculated at the first location is greater than or equal to the difference calculated at the second location, the height data at the first location is deleted.

[0024] The present invention may be that, in the step of judging an error, if the reduction interval is included in the slow growth stage or the growth plate-free stage, the data of the reduction interval is deleted according to the duration of the reduction interval and the degree of height reduction.

[0025] An exemplary embodiment of the present invention may provide a program stored in a computer-readable recording medium including a program code for executing the above-mentioned method for preprocessing height measurement data.

[0026] An exemplary embodiment of the present invention may provide a computer-readable recording medium having recorded therein a program for executing the above-mentioned method for preprocessing height measurement data for predicting growth.

[0027] An exemplary embodiment of the present invention may provide a preprocessing device for predicting height measurement data of growth, which is a preprocessing device for predicting height measurement data of growth, and includes: a first input unit, which inputs the time series biological component data of the subject; a second input unit, which inputs the identification data of the subject; a height data extraction unit, which extracts the height data from the biological component data; a decrease interval judgment unit, which is used to judge whether there is an interval in which the height data decreases within the input interval; a growth stage classification unit, which, when there is an interval in which the height data decreases, classifies the growth stage containing the decrease interval; and an error judgment unit, which judges the error of the height data according to the growth stage containing the decrease interval.

[0028] The present invention may further include a connecting unit for connecting the data inputted through the first input unit and the second input unit to generate the first data.

[0029] The present invention may further include an error detection unit, which deletes the biological component data when a preset value is measured in the biological component data in the first data.

[0030] Unless the features of the above-mentioned embodiments are inconsistent with or exclusive of other embodiments, they may be combined and embodied in other embodiments.

[0031] Effects of the Invention

[0032] According to various embodiments of the present invention, when predicting the growth of a child or adolescent, the accuracy of the growth prediction can be improved by determining errors in the biological component data measured to predict the growth of the child or adolescent.

[0033] According to various embodiments of the present invention, the accuracy of growth prediction can be improved by determining the interval in which height reduction occurs and eliminating erroneous data that may occur when measuring the height of the subject. In addition, when the interval in which height reduction occurs is determined to be erroneous, the erroneous data can be determined more effectively by considering the growth stage of the child or adolescent.

[0034] According to various embodiments of the present invention, when providing a solution to a child or adolescent's growth needs, the accuracy of growth prediction can be improved by eliminating errors in input data.

[0035] According to various embodiments of the present invention, the accuracy of growth prediction of a child or adolescent may be improved by determining errors in biological component data measured for predicting the growth of the child or adolescent and generating refined input data.

[0036] According to various embodiments of the present invention, the accuracy of the solution required for the growth of a child or adolescent can be improved by determining errors in the biological component data measured to generate the solution required for the growth of the child or adolescent and generating refined input data.

[0037] The effects of the present invention are not limited to the above-mentioned effects, and those skilled in the art will clearly recognize other effects not mentioned from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a diagram illustrating a structure of a growth prediction and solution generating apparatus according to an exemplary embodiment of the present invention.

[0039] Figure 2 It is shown Figure 1 Figure 2. Data preprocessing section of the .

[0040] Figure 3 is a graph illustrating predicted height and target height by growth stage according to an exemplary embodiment of the present invention.

[0041] Figure 4 is a graph illustrating obesity and growth at normal weight by growth stage of boys according to an exemplary embodiment of the present invention.

[0042] Figure 5 is a graph illustrating obesity and growth at normal weight according to growth stages of girls according to an exemplary embodiment of the present invention.

[0043] Figure 6 is a diagram illustrating a structure of a neural network for growth prediction and solution provision according to an exemplary embodiment of the present invention.

[0044] Figure 7 is a diagram illustrating a first neural network model according to an exemplary embodiment of the present invention.

[0045] Figure 8 is a diagram illustrating a second neural network model according to an exemplary embodiment of the present invention.

[0046] Figures 9 to 11 is a diagram illustrating a preprocessing method of height measurement data for growth prediction according to an exemplary embodiment of the present invention.

[0047] Fig.12 and Fig.13 is a diagram visually illustrating a pre-processing process of input data according to an exemplary embodiment of the present invention.

[0048] Fig.14 is a graph showing biological component data of a subject according to an exemplary embodiment of the present invention.

[0049] Fig.15 is a diagram showing data connecting biological component data and identification data of a subject according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following is a description of the specific implementation of the present invention with reference to the accompanying drawings. The following specific description is provided to help a comprehensive understanding of the methods, devices and / or systems described in this specification. However, these are only examples, and the present invention is not limited thereto.

[0051] When describing the embodiments of the present invention, if it is judged that the specific description of the known technology related to the present invention will make the gist of the present invention unclear, the detailed description thereof will be omitted. In addition, the terms described below are defined in consideration of the functions in the present invention, and can be changed according to the intention or habit of the user or operator. Therefore, the terms should be defined based on the contents in the entire specification.

[0052] The terms used in the detailed description are only for explaining the embodiments of the present invention and are not limiting. Unless clearly used otherwise, expressions in the singular form include the meaning of the plural form.

[0053] In this specification, expressions such as "comprise" or "have" should be interpreted as referring to certain characteristics, numbers, steps, actions, elements, parts of these, or combinations thereof, and do not exclude the existence or possibility of one or more other characteristics, numbers, steps, actions, elements, parts of these, or combinations thereof other than those mentioned.

[0054] In addition, when describing the components of the embodiments of the present invention, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only used to distinguish the components from other components, and the nature, order, sequence, etc. of the components are not limited by these terms.

[0055] Figure 1 is a diagram showing a structure of a growth prediction and solution generation apparatus according to an exemplary embodiment of the present invention, Figure 2 It is shown Figure 1 Figure 1. Data preprocessing part of the diagram. Fig.14 is a diagram showing biological component data of a subject according to an exemplary embodiment of the present invention, Fig.15 is a diagram showing data connecting biological component data and identification data of a subject according to an exemplary embodiment of the present invention.

[0056] Below, refer to Figure 1 , Figure 2 , Fig.14 and Fig.15 Provide explanation.

[0057] The growth prediction system of this embodiment can receive input after preprocessing the time series physical data of the subject. In addition, the gender of the subject can be classified based on the input physical data, and the growth prediction can be predicted or a solution through growth prediction can be generated according to the gender of the subject through different growth prediction models.

[0058] The growth rate by growth stage may be different for boys and girls. For example, as described below, the period when boys and girls enter the rapid growth stage may be different, and therefore, the growth rate or the generation of a solution considering the growth rate may be different. Therefore, preferably, it is necessary to perform growth prediction using a model that learns different learning data based on the subject's gender.

[0059] More specifically, the growth prediction and solution generation device according to an exemplary embodiment of the present invention may include a data preprocessing unit 100, an input unit 10, a gender judgment unit 20, a growth stage judgment unit 30, a prediction unit 50, a solution generation unit 70, and a display unit 90.

[0060] The growth prediction and solution generation device can obtain the time series physical data of the subject by inputting it through the input unit 10.

[0061] The subject's body data may include data that can identify the subject (identification data), and the subject's biological component data. The identification data and biological component data may be composed of time series data.

[0062] Exemplarily, the identification data may include data for identifying the subject, such as grade (or age), gender and height, and the biological component data may include data on weight, protein, mineral content, body fat, body water, muscle mass, body fat mass, bone tissue, skeletal muscle mass, body mass index, basal metabolic rate, neck circumference, chest circumference, abdominal circumference, thigh circumference, arm circumference and hip circumference.

[0063] More specifically, refer to Fig.14 and Fig.15 The subject's identification data 1201 may include date of birth 1201-1, gender 1201-2, age in months 1201-3 and measurement date 1201-4.

[0064] In addition, the subject's biological component data 1101 may include data number 1101-1, height 1101-2, weight 1101-3, protein content 1101-4, mineral content 1101-5, body fat mass 1101-6 (Body Fat Mass), muscle mass 1101-7 (soft lean mass), bone mineral mass 1101-8 (Osseous mineral), and skeletal muscle mass 1101-9.

[0065] These body data are merely examples to help understand the present invention, and the present embodiment is not limited thereto. Of course, the types of data constituting the body data may be changed in various ways according to the embodiment.

[0066] In addition, exemplary embodiments of the present invention may include a data preprocessing unit 100 for determining errors in the subject's physical data and generating refined data.

[0067] Since the biological component data 1101 may change rapidly according to the growth stage due to the physical characteristics of children or adolescents, errors (errors in the measurement process) may occur in the measurement process of other biological component data, such as measurement method, measurement period, measurement environment, etc.

[0068] In addition, during the transmission or storage of the measured biological component data, errors (data transmission errors) may occur.

[0069] Therefore, after the data preprocessing unit 100 of this embodiment determines whether there is an interval in which the height 1101-2 decreases in the measured biological component data 1101, if there is an interval in which the height data decreases, it can determine the error of the height data 110-2 in the identification data 1201 by taking into account the growth stage of the subject through the monthly age data 1201-3.

[0070] As described above, when there is a decrease in the biological component data 1101, especially in the height data 1101-2, the refined data can be generated by judging the error in the measurement process and the data transmission error.

[0071] As described above, the data preprocessing unit 100 of this embodiment can generate refined data (second data) by determining errors in the input biological component data 1101 and the identification data 1201. The second data generated in this way can be used as input data of the input unit 10 to predict the growth of the subject or generate a solution through growth prediction.

[0072] In addition, the data preprocessing unit 100 may include: a biological component data input unit 110, an identification data input unit 120, an error detection unit 130, a connection unit 140, a height data extraction unit 151, a reduction interval judgment unit 153, a growth stage classification unit 155, a data deletion unit 180, and a second data generation unit 190.

[0073] The data preprocessing unit 100 may obtain the biological component data 1101 of the subject through the biological component data input unit 110 , and may obtain the identification data 1201 of the subject through the identification data input unit 120 .

[0074] The error detection unit 130 may detect whether a preset value exists in the input biological component data 1101 of the subject, and the preset value may be exemplarily set to a "Nan" value, ie, a measurement error.

[0075] The connection unit 140 may generate the first data 140d by connecting the subject's biological component data 1101 and the identification data 1201. The first data 140d is data before being refined by the error determination unit 170, and can be distinguished from the second data refined by the error determination unit 170.

[0076] The height data extraction unit 151 may extract the height data 1101 - 2 from the biological component data 1101 of the inputted subject, and determine whether there is a decreasing interval in the extracted height data through the decreasing interval determination unit 153 .

[0077] The growth stage classification unit 155 may classify the growth stage of the subject based on the input identification data 1201 of the subject.

[0078] When the decrease interval determination unit 153 determines that the extracted height data has a decrease interval, the error determination unit 170 may determine that the height data is incorrect.

[0079] More specifically, the error in the height data may be judged by setting different criteria for error based on the growth stage of the subject classified by the above-mentioned growth stage classification unit 155 and the growth stage including the decrease interval of the height data.

[0080] The data deleting section 180 may delete the data determined to be erroneous by the error determining section 170 , and the second data generating section 190 may generate refined data (second data) after determining an error in the input data.

[0081] Reference Figures 9 to 11 The method of preprocessing the height measurement data for growth prediction by the above-mentioned data preprocessing unit is described in more detail.

[0082] The data input to the input section 10 may include time-series body data of the subject.

[0083] The time series body data may be continuous data or discontinuous data, but may be data included in at least one of the periods corresponding to the growth stages of children or adolescents.

[0084] More specifically, the collection period and the collection frequency of the subject's time-series physical data may vary.

[0085] For example, the first subject may have physical data measured from 8 to 12 years old, which is part of the childhood or adolescence period, and the second subject may have physical data measured irregularly, such as at 8 years old, 10 to 12 years old, 15 years old, etc.

[0086] In addition, the third subject may have physical data measured multiple times within a certain period (during one of the growth stages of a child or an adolescent), while the fourth subject may have physical data measured only once within a certain period of time (during one of the growth stages of a child or an adolescent).

[0087] As described above, depending on the collection period and the number of collections, the subject's physical data may include two or more growth stages of children or adolescents (first subject, second subject), but may not be the case (third subject, fourth subject).

[0088] As for the third subject as described, when there are multiple measured physical data during one growth stage among multiple growth stages, the growth stage judgment unit 30 classifies the growth stage including the physical data of the third subject through the growth stage classification unit 31, and can extract the physical data through the physical data extraction unit 33. The physical data extracted here can generally include the physical data of the third subject input through the input unit 10.

[0089] However, as in the case of the fourth subject, when the subject's body data corresponds to only one of multiple growth stages and there is body data measured only once during the period, body data corresponding to any period before the growth stage judgment unit 30 inputs the body data of the fourth subject can be further generated.

[0090] More specifically, the inputted physical data of the subject may be used to generate time-series physical data corresponding to the subject in any period based on the time-series physical growth information of a plurality of sample subjects stored in advance.

[0091] As an example, physical data can be generated based on the input subject's physical data and a distribution model (similarity) of time series physical growth data of multiple sample subjects stored in advance, and physical data can also be generated based on a Bayesian inference model (conditional probability).

[0092] The growth stage determination unit 30 can classify the subject's body data input through the input unit 10 into any one of a plurality of growth stages in the growth stage classification unit 31 , and can extract body data corresponding to the classified growth stage in the body data extraction unit 33 .

[0093] Figure 3 is a graph illustrating predicted height and target height by growth stage according to an exemplary embodiment of the present invention.

[0094] Figure 3 The x-axis in the graph represents age, months, and the y-axis represents height (cm). In contrast, the dotted line P represents the predicted growth rate of the subject, and the solid line G represents the target growth rate for evaluating the subject.

[0095] Reference Figure 3 The growth stages of the children or adolescents described above may include a general growth stage (301), a rapid growth stage (303), a slow growth stage (305), and a growth plate-free stage (307).

[0096] Each growth stage can be distinguished according to the growth rate. The height growth each year is different in each growth stage. Even in the same growth stage, the actual height growth will vary depending on the growth type.

[0097] The general growth stage (301) usually refers to the period before puberty when secondary sexual characteristics appear. During this period, the growth plates of children or adolescents are generally open, so depending on the growth environment, the shorter growth type will generally grow 4 to 5 cm per year, while the taller growth type will grow 6 to 7 cm per year.

[0098] The rapid growth phase (303) is when secondary sexual characteristics begin to appear. Girls' breasts swell and lumps appear, boys' testicles enlarge, pubic hair begins to grow, and voice changes. The rapid growth phase (303) lasts about 2 to 3 years after the normal growth phase (301), with an average annual growth of 7 to 10 cm.

[0099] The slow growth stage (305) refers to the period when secondary sexual characteristics are completed. During this period, girls can be distinguished by the menarche, while boys can clearly see their changes through pubic hair, changes in voice, and armpit hair. If the growth rate drops rapidly in the slow growth stage (305) compared to the rapid growth stage (303), it generally lasts for about 2 to 3 years, with an average annual growth of about 5 to 6 cm, and then naturally stops growing. The growth plates begin to gradually close after the rapid growth stage (304), and about 6 months after entering the slow growth stage (305), the growth plates are closed by about 50%.

[0100] The no growth plate stage (307) refers to the period when the growth plates have closed. Although the growth period has not completely ended, it has become difficult to grow taller naturally. Generally, for girls, the no growth plate stage (307) is entered within about 1 year and 6 months to 2 years from the time of menarche, and for boys, the no growth plate stage (307) is entered within about 1 year and 6 months to 2 years from the time of armpit hair growth. In the no growth plate stage (307), although the growth plates have closed and natural growth has stopped, by changing incorrect living habits and improving body functions through customized exercises, posture correction and nutritional intake, the height can also be increased by about 1 to 3 cm.

[0101] The prediction unit 50 is a prediction model that can be implemented using artificial intelligence with a recursive neural network (RNN) structure, so that not only current values ​​but also time series values ​​can be used. For example, the prediction model can be implemented using a recursive neural network, a long short-term memory (LSTM), or a gated recurrent unit (GRU). Of course, in addition to this, various previous artificial intelligence architectures can also be applied to the prediction model of this embodiment, which will be referred to in detail later. Figures 6 to 8 Provide detailed explanation.

[0102] The solution generating section 70 may generate a growth management solution based on the body data of the subject corresponding to the classified growth stage.

[0103] More specifically, when the subject is in the general growth stage (301), a solution for increasing the growth prediction value of the subject can be provided. The growth prediction value corresponds to Figure 4 The value of the y-axis in the figure can provide the subject with solutions for increasing the predicted growth value through various solution display units 90 for increasing the predicted target value of the y-axis.

[0104] Examples of solutions provided by the display unit 90 may include current height, predicted height, obesity level, body fat mass, skeletal muscle mass, protein mass, mineral mass, sleep volume, exercise volume, nutritional information, living habits, posture, etc. Each indicator may be gradually expressed as attention, normal, good, etc. according to a preset range, or may also be expressed as a level.

[0105] In addition, the current status, customized solutions, and precautions for each indicator can be displayed. The current status can be displayed by stage or level according to the target value. If it is a customized solution, it can include information such as protein, mineral content, body fat, body water, muscle mass (soft lean mass), body fat mass (fat free mass), bone tissue, skeletal muscle mass, body mass index (BMI, Body Mass Index), basal metabolic rate, etc. that need to be adjusted to achieve the current target value based on the input body data.

[0106] If attention is paid, based on the input body data, it may include information such as the current insufficient protein, mineral content, body fat, body water, muscle mass (soft lean mass), body fat mass (fat free mass), bone tissue, skeletal muscle mass, body mass index (BMI, Body Mass Index), basal metabolic rate, etc. that need to be adjusted.

[0107] In addition, when the subject is in a rapid growth stage (303), a solution for increasing the growth prediction value of the subject in the rapid growth stage (303) may be provided. The growth prediction value corresponds to Figure 2 The value of the y-axis in the figure can provide the subject with solutions for increasing the predicted growth value through various solution display units 90 for increasing the predicted target value of the y-axis.

[0108] Figure 4 is a graph showing obesity and growth at normal weight according to growth stages of boys according to an exemplary embodiment of the present invention, Figure 5is a graph illustrating obesity and growth at normal weight according to growth stages of girls according to an exemplary embodiment of the present invention.

[0109] Figure 4 The x-axis represents age, and the y-axis represents growth rate in cm. The solid line G1 represents the growth rate of each growth stage when boys are normal weight, and the dotted line P1 represents the growth rate of each growth stage when boys are obese.

[0110] Figure 5 The x-axis in the graph represents age, and the y-axis represents growth rate in cm. The solid line G2 represents the growth rate of each growth stage when a girl is of normal weight, and the dotted line P2 represents the growth rate of each growth stage when a girl is obese.

[0111] Next, we will refer to Figure 4 and Figure 5 Provide explanation.

[0112] Obesity is not simply weight gain, but a disease accompanied by excessive accumulation of fat tissue in the body, accompanied by overweight or metabolic disorders caused by it. Medically, childhood or adolescent obesity is defined as a situation in which the weight of a child from infancy to adolescence exceeds the standard weight for height by more than 20%.

[0113] Most cases of obesity in infancy disappear after the baby's first birthday as the child becomes more active and physically active. However, in some cases, the obesity persists, and in many cases, the obesity temporarily returns to normal only to relapse during school age.

[0114] 75% to 80% of childhood and adolescent obesity will turn into adult obesity. In addition, it will inhibit the secretion of growth hormone, especially for girls, which will advance puberty and shorten growth potential, thus leading to growth stunting or causing precocious puberty.

[0115] Therefore, when obesity is predicted in school-age children or adolescents who are prone to obesity and obesity is predicted, it is necessary to provide a solution that can systematically prevent obesity.

[0116] In addition, childhood or adolescent obesity can be divided into simple obesity of unknown cause and symptomatic obesity caused by special causes, among which more than 99% of childhood obesity is simple obesity.

[0117] The general characteristics of simple obese children are that during the normal growth stage (301), both boys and girls are average or slightly taller than their peers (multiple sample subjects), but after the rapid growth stage (303), they will be shorter than their peers (multiple sample subjects) or have a lower growth rate.

[0118] In summary, childhood or adolescent obesity can be understood as a group of diseases caused by various reasons accompanied by excessive weight or metabolic disorders. Figure 5 and Figure 6 In general, boys and girls together have an average or slightly higher growth rate than multiple sample subjects of the same age during the general growth stage (301), but after the rapid growth stage (303), they show a growth rate that is often lower than multiple sample subjects of the same age.

[0119] Therefore, in this embodiment, in order to more accurately predict obesity and generate a solution based on the growth stage, the gender can be classified by the gender judgment unit 20 based on the physical data of the subject input through the input unit 10, and then the growth stage can be classified in the growth stage judgment unit 30 according to the classified gender, and the physical data can be extracted. Then, the solution generation unit 70 generates a solution in consideration of the gender and growth stage of the subject.

[0120] More specifically, in the case where both boys and girls are obese, it can be confirmed that the growth prediction value in the rapid growth stage (303) is lower than normal. Therefore, when the subject is in the rapid growth stage (303), a solution for increasing the growth prediction value can be provided, and when the subject is in the normal growth stage (301), the considered physical data can be included, especially the content related to the adjustment of indicators that can alleviate the abnormal increase of sex hormones.

[0121] In addition, when the subject belongs to the slow growth stage (305), a solution for adjusting the period of the slow growth stage (305) of the subject can be provided. Adjusting the period of the growth stage can distinguish between a case where the subject's physical data is in the initial stage of the slow growth stage (305) and a case where it is in the middle or late stage of the slow growth stage (305) in the growth stage classified based on the input physical data of the subject.

[0122] The criteria for distinguishing the initial stage and the middle and late stages of the above-mentioned slow growth stage (305) can be as follows: Figures 4 to 6 The x-axis in the figure is used as the basis, and the distinction is made based on the predetermined range from the rapid growth stage (303) to the slow growth stage (305), or based on whether the secondary sexual characteristics have ended according to the input physical data of the subject. If it has not ended, it can be distinguished as the initial stage of the slow growth stage (305), and if it has ended, it can be distinguished as the middle and late stages of the slow growth stage (305).

[0123] Preferably, it is possible to determine whether the secondary sexual characteristics have ended based on the input physical data of the subject, so as to determine whether the subject's current physical data is in the initial stage or the middle and late stage of the slow growth stage (305). When it is impossible to determine whether the secondary sexual characteristics have ended based on the input physical data of the subject, it is possible to determine whether the subject's physical data is in the initial stage or the middle and late stage of the slow growth stage (305) based on the predetermined range from the rapid growth stage (303) to the slow growth stage (305).

[0124] In addition, when the input physical data of the subject is in the initial stage of the slow growth stage (305), a period adjustment plan can be provided to delay entering the slow growth stage (305).

[0125] As described above, the period when the secondary sexual characteristics transition from the rapid growth stage (303) to the slow growth stage (305) gradually ends. Therefore, a solution for delaying the completion time of the secondary sexual characteristics can be provided, and the display unit 90 can be used to provide the subject with a display of the secondary sexual characteristics. Figures 4 to 6 Various solutions for moving the range of the x-axis corresponding to the slow growth stage (305) to the right. In this case, the range of the slow growth stage (305) can be increased according to the physical data of the subject, or the range can be reduced as the fast growth stage (303) increases.

[0126] In addition, if the input physical data of the subject is in the middle and late stages of the slow growth stage (305), a period adjustment plan can be provided to increase the period of the slow growth stage (305).

[0127] As described above, the slow growth stage (305) refers to the period when the growth plate of the subject is closed. Generally, 6 months after entering the slow growth stage (305), the growth plate is closed by about 50%. When the growth plate is closed and natural growth stops, the subject enters the no growth plate stage (307). Therefore, in this case, a solution for increasing the period of the slow growth stage (305) can be provided. In other words, the display unit 90 can be used to provide the subject with a solution for increasing the period of the slow growth stage (305). Figures 4 to 6 Various solutions for the range of the x-axis corresponding to the slow growth phase (305).

[0128] When the above-mentioned subject belongs to the general growth stage (301), the physical data that needs to be considered may include, in particular, content related to adjusting indicators that can alleviate the degree of growth plate closure.

[0129] In addition, when the subject is in the growth plate-free stage (307), solutions such as improving physical functions through lifestyle habits, customized exercises, posture correction, nutritional intake, etc. can be provided based on the input physical data of the subject.

[0130] In the growth plate-free stage (307), the growth plates are closed and natural growth stops. Therefore, solutions to improve physical functions such as lifestyle habits, customized exercises, and posture correction can be provided based on the subject's weight, body fat, body water, muscle mass, skeletal muscle mass, body mass index (BMI), basal metabolic rate, neck circumference, chest circumference, abdominal circumference, thigh circumference, arm circumference, hip circumference, etc., or solutions to improve physical functions through nutritional intake can be provided based on protein, mineral content, bone tissue (bone density), etc.

[0131] In addition, in order to more accurately predict obesity and provide a solution in this embodiment, the gender of the subject is classified by the gender determination unit 20, and the growth stage classification unit 31 sets the period of the rapid growth stage differently according to the classified gender.

[0132] This is because, as described above, boys and girls enter the rapid growth stage at different times. Also, the body data of the subject corresponding to the rapid growth stage considering gender output from the body data extraction unit 33 is input to the prediction unit 50 to output a predicted value of obesity.

[0133] When the subject is predicted to be obese, and the gender is a boy, a solution for increasing the growth prediction value of the subject in the rapid growth stage (303) may be provided, which is as described above.

[0134] Also, when the subject is predicted to be obese and the gender is a girl, a solution for increasing the duration of the rapid growth phase (303) may be provided.

[0135] For more details, refer to Figure 4 and Figure 5 For girls, the difference in predicted growth values ​​during the rapid growth phase (303) is smaller than that for boys, but as the rapid growth phase (303) transitions to the slow growth phase (305), the speed of growth (growth rate) at each age group decreases significantly.

[0136] For example, in Figure 4 It can be seen that the difference in the growth prediction value of a normal-weight boy in the rapid growth stage (303) is significantly different from the growth prediction values ​​in the normal-weight case (G1) and the obese case (P1). In addition, a solution for increasing the growth prediction value in the rapid growth stage (303) is required for obese boys.

[0137] Also, as an example, Figure 5 In the study, normal-weight girls grew 7 cm from age 9 to 10, and about 6.5 cm from age 10 to 11.

[0138] In contrast, obese girls grew about 6.7 cm taller between the ages of 9 and 10, and about 5.7 cm taller between the ages of 10 and 11. Figure 5 As can be seen, the difference in growth predictions is relatively small compared to the case of boys.

[0139] Therefore, for obese girls, it is necessary to provide a solution for increasing the duration of the rapid growth phase (303) to reduce the amount of growth rate reduction that occurs when transitioning from the rapid growth phase to the slow growth phase.

[0140] Figure 6 is a diagram showing the structure of a neural network for growth prediction and solution provision according to an exemplary embodiment of the present invention, Figure 7 is a diagram showing a first neural network model according to an exemplary embodiment of the present invention, Figure 8 is a diagram illustrating a second neural network model according to an exemplary embodiment of the present invention.

[0141] Below, refer to Figures 6 to 8 Provide explanation.

[0142] An exemplary embodiment of the present invention may include a first model 50 and a second model 13 , and may construct a pipeline in which at least a portion of an output of the second model 13 is input to the first model 50 .

[0143] More specifically, the first model 50 is a model that learns body data corresponding to at least one of a plurality of growth stages based on time-series body data of a plurality of sample subjects as learning data.

[0144] The growth stage may be a rapid growth stage (303) which is a period when growth begins to slow down due to obesity, but as described above, any one or more of a plurality of growth stages may be used in order to more accurately predict obesity.

[0145] The first model 50 includes LSTM neural networks 50-1, 50-2, 50-3, and 50-4 for learning time series data, and uses the past physical data of multiple sample subjects to train the LSTM neural networks 50-1, 50-2, 50-3, and 50-4. Then, the physical data of the current subject is input into the trained LSTM neural networks 50-1, 50-2, 50-3, and 50-4, and the predicted growth rate and the solution considering the growth rate are output.

[0146] The subject's body data may refer to data refined by the data preprocessing unit 100 after the error judgment process, which will be processed by the following Fig.10 and Fig.11Provide detailed explanation.

[0147] LSTM neural networks 50-1, 50-2, 50-3, and 50-4 are learned using at least one of the body data of multiple sample subjects as a default value. For example, for height, training is performed using height data of each year in an arbitrary period or a specific growth stage, and predictions for the next year are made and compared with the actual data. By comparing in this way, the training set is moved to the future in units of an arbitrary period or a specific growth stage and learned.

[0148] In addition, the LSTM neural networks 50-1, 50-2, 50-3, and 50-4 can be learned for each growth stage. Therefore, each of the general growth stage (301), the rapid growth stage (303), the slow growth stage (305), and the growth plate-free stage (307) can be learned as past body data of the corresponding growth stage.

[0149] The body data of the subject used for the study may refer to the data refined by the data preprocessing unit 100 after the error judgment process, which will be processed by the following method. Fig. 9 and Fig.13 Provide detailed explanation.

[0150] In addition, exemplarily, in this embodiment, time series physical data of multiple sample subjects can be input in order according to age or any period as learning data, and the calculation results of the predicted values ​​of the past time points or growth rates can be transmitted to the growth rate prediction of the next age or any period.

[0151] Therefore, the LSTM neural networks 50-1, 50-2, 50-3, and 50-4 can not only predict the growth rate based on the current physical data, but can also learn the extent to which the prediction results of various indicators at past time points affect the current growth rate prediction. Thus, the items that have the greatest impact on the growth rate change can be extracted based on the age or any period in the indicator and reflected in the growth rate prediction.

[0152] In addition, for time series learning, it is necessary to obtain the physical data of multiple sample subjects regularly. However, as described above, it may be difficult to obtain the physical data of multiple sample subjects regularly at an age or an arbitrary period, so outlier physical data or non-continuous physical data of each unit period can be removed and normalized in time for use.

[0153] In addition, the second model 13 can derive bone maturity (age) from the hand bone image using a convolutional neural network (Convolution Neural Network) learned using the subject's bone maturity data as learning data.

[0154] More specifically, the convolutional neural network includes multiple convolution layers and pooling layers, wherein the multiple convolution layers extract feature maps of features in the analysis object image in the hand bone image, and the pooling layers perform sub-sampling between the multiple convolution layers, thereby extracting features of different levels in the analysis object area, probabilistically inferring features through activation functions, or performing regression analysis to derive bone maturity through weight learning between nodes.

[0155] The bone maturity extracted by the second model 13 is input into the LSTM neural networks 50-1, 50-2, 50-3, and 50-4 together with at least a portion of the subject's physical data, which can improve the accuracy of the subject's growth rate prediction, thereby further improving the accuracy of the prediction of the subject's obesity, precocious puberty, growth, etc.

[0156] Figures 9 to 11 is a diagram illustrating a method for preprocessing height measurement data for growth prediction according to an exemplary embodiment of the present invention, Fig.12 and Fig.13 is a diagram visually illustrating a pre-processing process of input data according to an exemplary embodiment of the present invention.

[0157] As described above, the physical data input when predicting the growth of a subject (child or adolescent) or providing a solution through growth prediction may have errors in the measurement process or data transmission.

[0158] Therefore, in order to more accurately predict the growth of the subject or provide a solution through growth prediction, the refined data that has undergone the above-mentioned process of detecting and judging erroneous data needs to be input into the input unit 10 .

[0159] In addition, the physical data of the plurality of sample subjects used to train the prediction model of the prediction unit 50 may also use refined data that has undergone the above-mentioned process of detecting and judging erroneous data.

[0160] Below, refer to Figure 2 , Figures 9 to 13 Describe the process of detecting and correcting errors in height measurement data.

[0161] After receiving the body data ( S10 ) in order to detect or determine errors in the body data of a subject, especially the body height data, the exemplary embodiment of the present invention extracts the body height data from the input body data ( S30 ).

[0162] Then, based on the extracted height data, it is determined whether there is an interval in which the height decreases (S50). If there is an interval in which the height decreases (S50: Yes), the growth stage in which the height decrease occurs can be classified (S70). Therefore, through the above step (S70), it can be classified whether the interval in which the height decrease occurs belongs to the above-mentioned general growth stage (301), rapid growth stage (303), slow growth stage (305), and no growth plate stage (307).

[0163] In addition, the growth stage may be considered to determine errors in the body data (S90).

[0164] More specifically, if Fig.15 As shown, the subject's body data may include identification data 1201 and biological component data 1101, and the preprocessing unit 100 can obtain the subject's biological component data 1101 (S111) through the biological component data input unit 110, and can obtain the subject's identification data 1201 (S112) through the identification data input unit 120.

[0165] In addition, the input biological component data 1101 and the identification data 1201 may be generated as concatenated data 140d by the concatenation unit 140. The data 140d may be referred to as first data to distinguish it from the second data refined by the data preprocessing unit 100 of this embodiment.

[0166] When the connected data 140d is determined to have detected an error in the biological component data 1101 input after the error detection unit 130 detects an error in the biological component data (S131) ​​(S131, yes), at least a portion of the biological component data constituting the connected data 140b may be deleted (S132).

[0167] Exemplarily, when at least one of the plurality of items 1101-1 to 1101-9 constituting the input biological component data 1101 includes a "Nan" value, the data preprocessing unit 100 of this embodiment may delete the biological component data 1101 through the data deleting unit 180. In this case, based on the measurement date 1201-4 of the identification data, all items (rows including the measurement date) measured on the measurement date including the above-mentioned "Nan" value may be deleted.

[0168] Alternatively, when the age in months 1201-3 of the identification data is less than or equal to 30 months, all items measured on the measurement date containing the 'Nan' value (rows containing the measurement date) can be deleted. In more detail, when the age in months is less than or equal to 30 months, this is a period of rapid growth for infants, and there may be many errors in the measurement. Therefore, the reliability of the biological component data is lower than that of the biological component data with a relatively high age. Therefore, as described above, if the biological component data contains the "Nan" value when the age in months is less than or equal to 30 months, all items measured on the measurement date containing the "Nan" value can be deleted.

[0169] The above-mentioned deletion of the biological component data is exemplary, and when errors (for example, Nan values) are included in items constituting the biological component data according to various standards, the errors may be detected and deleted.

[0170] If no error is detected in the biological component data 1101 ( S131 : No), the height data extraction unit 151 may extract the height data 1101 - 2 .

[0171] In addition, whether there is a decreasing interval in the extracted height data can be determined by the decreasing interval determination unit 153 (S511). If there is no decreasing interval in the height data (S511: No), it is a case that there is no error in the height data, and therefore, the second data generation unit 190 can generate refined second data that can be input to the input unit 10 or the prediction module (S5112).

[0172] On the other hand, if there is a decreasing interval of the height data (S511: Yes), the growth stage including the decreasing interval of the height data can be classified by the growth stage classification unit 155 (S711), and the error of the biological component data can be determined.

[0173] More specifically, the growth rates of children or adolescents in different growth stages are different, so the magnitude of height changes is also different. For example, the magnitude of height changes in the rapid growth stage (303) is usually greater than the magnitude of height changes in the normal growth stage (301).

[0174] Therefore, when judging the error of the height data, different standards can be used when the decrease interval of the height data belongs to the rapid growth stage (303) and when the decrease interval of the height data belongs to the general growth stage (301).

[0175] More specifically, in this embodiment, after the growth stages including the decreasing interval of the height data are classified (S711), it is determined whether the decreasing interval is included in the rapid growth stage (S713).

[0176] If the decreasing interval is included in the rapid growth stage ( S713 : Yes), the data deleting unit 180 deletes all the height data forming the decreasing interval ( S7131 ), and generates the second data refined by the second data generating unit 190 .

[0177] The data deleted by the data deleting unit 180 may refer to the height data 1101 - 2 in the biological component data forming the reduction interval of the height data.

[0178] Alternatively, in order to improve the reliability of the data, all items (rows containing the measurement date) measured on the measurement date including the height data forming the decreasing interval of the height data based on the measurement date 1201-4 of the identification data may be deleted. That is, all data in the row containing the height data forming the decreasing interval of the height data may be deleted.

[0179] In addition, if the reduction interval is not included in the rapid growth phase (S713: No), it can be determined whether the reduction interval is included in the normal growth phase (S715).

[0180] The data error judgment standard when the reduction interval is included in the general growth stage (S715: Yes) may be set to be different from the data error judgment standard when the reduction interval is included in the fast growth stage (S713).

[0181] As described above, since the height change range in the rapid growth stage (303) is greater than the height change range in the general growth stage (301), if a decreasing interval occurs in the continuously measured height in the rapid growth stage (303), all height data forming the decreasing interval can be deleted, and if a decreasing interval occurs in the continuously measured height in the general growth stage 301, data with a higher probability of being erroneous data in the corresponding height data forming the decreasing interval can be deleted.

[0182] More specifically, when the decreasing interval is included in the general growth stage (S715: Yes), the error determination unit 170 may calculate the difference between the height of the first location and the height of the second location forming the decreasing interval and the average of the plurality of sample subjects, respectively. The first location and the second location may represent the starting point and the end point of the decreasing interval.

[0183] The difference value of the first location calculated in the step (S7151) is compared with the difference value of the second location (S7153). If the difference value of the first location is greater than the difference value of the second location (S7153: Yes), the second data (S7155) can be generated after deleting the height data of the first location (S7154). If the difference value of the first location is less than or equal to the difference value of the second location (S7153: No), the second data (S7157) can be generated after deleting the height data of the second location (S7156).

[0184] In the step (S7153), when the difference value at the first location is the same as the difference value at the second location, the difference value at the second location is deleted because the height data at the second location is measured later than the height data at the first location and is therefore closer to the rapid growth stage (303). Therefore, the height data measured at the second location is more likely to be wrong.

[0185] Fig.12 and Fig.13 It is a diagram visually showing a data preprocessing process in a case where a decreasing interval of height data is included in a general growth stage. Fig.12 and Fig.13 In the figure, the x-axis represents age in months (1201-3), the y-axis represents height (1101-2 (cm), and the dotted lines r1 and r2 represent the height data of the subjects. Fig.12 In FIG. 1 , the solid line e represents data obtained by fitting the physical data (growth curves) of a plurality of sample subjects with a specific function.

[0186] Fig.12 In , m2 represents the starting point (first location) of the decreasing interval of the height data, and m3 represents the ending point (second location) of the decreasing interval of the height data. Fig.12 As shown, the data preprocessing unit 100 of this embodiment can calculate the difference between each point m2, m3 and the average of multiple sample subjects when the reduction intervals m2, m3 are included in the general growth stage.

[0187] As described above, the difference value can be calculated by the average difference of the height data of a plurality of sample subjects corresponding to the height data of the subject at the first location m2 and the height data at the second location m3, such as Fig.12 As shown, it can be calculated by taking the difference between the physical data (growth curve) of multiple sample subjects and the data e fitted with a specific function.

[0188] The body data of the plurality of sample subjects may be pre-stored in the error judgment unit 170 to calculate the difference between the height data of the subject and the height data of the plurality of sample subjects, or the body data of the plurality of sample subjects may be fitted with a specific function to calculate the difference between the height data of the subject and the height data of the plurality of sample subjects. Of course, the above calculation may be performed by other calculation units (not shown) that may be included in the data preprocessing unit 100.

[0189] In this example, since the difference calculated at the first location m2 is greater than the difference calculated at the second location m3 (S7153: No), the data preprocessing unit 100 can delete the height data at the first location m2 through the data deletion unit 180, and then generate refined second data through the second data generation unit 190.

[0190] In addition, when the decrease interval is not included in the general growth stage (S715: No), the error judgment unit 170 can compare the period of the decrease interval with the preset first period (S731). The step (S731) is a case where the decrease interval existing in the height data 1101-2 input through the above step is not included in the general growth stage (301) or the rapid growth stage (303), and therefore, it is a stage in the slow growth stage (305) or the growth plate-free stage (307).

[0191] Therefore, in the above case, the present embodiment can delete the data of the reduction interval according to the duration of the reduction interval and the degree to which the height data is reduced.

[0192] More specifically, if the duration of the reduction interval is less than the preset first duration (S731: Yes), the error determination unit 170 may calculate the height difference between the start point and the end point of the reduction interval (S7311). That is, in the step (S7311), the error determination unit 170 derives the degree of reduction of the height data (hereinafter, the height difference).

[0193] In addition, a step of comparing the height difference with a preset first reference value (S7313) may be performed.

[0194] If the height difference is greater than or equal to the preset first reference value (S7314: Yes), it means that within a shorter period (the period of the reduction interval <less than the first period> the decrease in height is greater than the first reference value. Therefore, the data preprocessing unit 100 can delete the height data of the reduction interval through the data deletion unit 180 (S7314), and generate the second data (S7315) through the second data generation unit 190.

[0195] The height data deleted in the step (S7314) may refer to the height data corresponding to the starting point of the reduction interval and the height data corresponding to the end point.

[0196] Alternatively, the height data deleted in the step (S7314) may refer to one of the height data corresponding to the starting point of the reduction interval and the height data corresponding to the end point. In this case, as described above, after the height data of each location is compared with multiple sample subjects, it is selectively deleted according to the difference.

[0197] In addition, as described above, the data deleted in the step (S7314) may include not only the height data corresponding to the start point or end point of the reduction interval but also all the data (rows) included in the measurement date of the height data.

[0198] Furthermore, if the height difference is less than the preset first reference value (S7313: No), the data preprocessing unit 100 may generate second data through the second data generating unit 190 (S7316).

[0199] Exemplarily, the first period may be set to 6 months, and the first reference value may be set to 1 cm.

[0200] In addition, if the duration of the reduction interval is equal to or greater than the preset first period (S731: No), the error determination unit 170 may determine whether the duration of the reduction interval is included in the range of the first period and the preset second period (S751).

[0201] If the reduction interval is greater than or equal to the first period but less than the second period (S751: Yes), the error determination unit 170 may calculate the height difference between the start point and the end point of the reduction interval (S7511). That is, in the step (S7511), the error determination unit 170 derives the degree to which the height data is reduced (hereinafter, the height difference).

[0202] In addition, a step of comparing the height difference with a preset second reference value (S7513) may be performed.

[0203] If the height difference is greater than or equal to the preset second reference value (S7314: Yes), the data preprocessing unit 100 can delete the height data of the reduced interval through the data deleting unit 180 (S7514), and can generate second data through the second data generating unit 190 (S7515).

[0204] Furthermore, if the height difference is less than the preset second reference value (S7513: No), the data preprocessing unit 100 may generate second data through the second data generating unit 190 (S7516).

[0205] Exemplarily, the second period may be set to 24 months, and the second reference value may be set to 0.5 cm.

[0206] In the step (S751), the case in which the period of the reduction interval of the height data is included in the range set in the first period and the second period refers to the case in which the difference in the number of measured months of the input data is greater than the case in which the reduction interval is less than the first period. Therefore, as described above, it is preferred to set the second standard to a value smaller than the first standard.

[0207] In addition, if the age of the subject exceeds the range set in the first period and the second period (S751: No), the difference in the number of measured months is relatively large. Therefore, the error judgment unit 170 judges that all the height data forming the reduced interval are erroneous, and after deleting all the height data through the data deletion unit 180 (S7531), the second data generation unit 190 generates the second data (S7533).

[0208] The preferred embodiments of the present invention are described above. All embodiments and condition examples disclosed in this specification are intended to help those skilled in the art understand the principles and concepts of the present invention. Therefore, it should be understood that those skilled in the art can implement variations within the scope of the essential features of the present invention.

[0209] Therefore, the disclosed embodiments should be considered from an illustrative point of view rather than a restrictive point of view. The scope of the present invention should be determined by the scope of the claims rather than the above description, and all differences within the scope of equivalence thereof should be included in the present invention.

[0210] In addition, the methods according to various embodiments of the present invention described above can be implemented by a program and then provided to a server or device. Therefore, each device can connect to a server or device storing a program and download the program.

[0211] In addition, the above-mentioned methods according to various embodiments of the present invention can be implemented by programs and stored in various non-transitory computer readable media (non-transitory computer readable medium). Non-transitory computer readable media refers to media that semi-permanently stores data and can be read by a device, rather than media that store data for a short time such as registers, caches, and memories. Specifically, the above-mentioned various applications or programs can be stored and provided on non-transitory computer readable media, such as CDs, DVDs, hard disks, Blu-ray discs, USBs, memory cards, ROMs, etc.

[0212] Although preferred embodiments of the present invention are illustrated above, the present invention is not limited to the above-mentioned specific embodiments, and various modifications may be made by a person skilled in the art without departing from the spirit of the present invention as claimed in the claims, and these modifications should not be understood separately from the technical ideas or prospects of the present invention.

Claims

1. A method for preprocessing height measurement data for predicting growth, which is a method for preprocessing height measurement data for predicting growth performed by a computing device, wherein: include: The step of inputting the time series biological composition data of the subject; A step of inputting identification data of the subject; The step of extracting height data from the biological component data; A step of determining whether there is a decreasing interval within the interval of the input height data; as well as When there is an interval in which the height data decreases, a step of determining an error in the height data according to the growth stage including the decrease interval.

2. The method for preprocessing height measurement data for predicting growth according to claim 1, wherein: The method further includes the step of generating first data by connecting the time series biological component data of the subject and the identification data of the subject.

3. The method for preprocessing height measurement data for predicting growth according to claim 2, wherein: The method further includes a step of deleting the biological component data after generating the first data if the biological component data includes a preset value.

4. The method for preprocessing height measurement data for predicting growth according to claim 1, wherein: The growth stages include a general growth stage, a rapid growth stage, a slow growth stage and a growth plate-free stage, and are classified according to the age in months of the identification data.

5. The method for preprocessing height measurement data for predicting growth according to claim 4, wherein: In the error determination step, when the decrease interval is included in the rapid growth stage, the height data of the decrease interval is deleted.

6. The method for preprocessing height measurement data for predicting growth according to claim 4, wherein: The error judgment step includes the step of calculating the difference between the height data of any first location and the height data of any second location forming the reduction interval and the average data values ​​of the corresponding months' age of multiple sample subjects independently when the reduction interval is included in the general growth stage.

7. The method for preprocessing height measurement data for predicting growth according to claim 6, wherein: The step of determining an error further includes the step of comparing the difference value calculated at the first location with the difference value calculated at the second location.

8. The method for preprocessing height measurement data for predicting growth according to claim 7, wherein: In the error determination step, if the difference calculated at the first location is smaller than the difference calculated at the second location, the height data at the second location is deleted.

9. The method for preprocessing height measurement data for predicting growth according to claim 7, wherein: In the error determination step, if the difference calculated at the first location is greater than or equal to the difference calculated at the second location, the height data at the first location is deleted.

10. The method for preprocessing height measurement data for predicting growth according to claim 4, wherein: In the error judgment step, if the decrease interval is included in the slow growth stage or the growth plate-free stage, the data of the decrease interval is deleted according to the duration of the decrease interval and the degree of height reduction.

11. A program stored in a computer-readable recording medium including a program code for executing the method of preprocessing height measurement data for predicting growth according to any one of claims 1 to 10.

12. A computer-readable recording medium having recorded therein a program for executing the method for preprocessing height measurement data for predicting growth according to any one of claims 1 to 10.

13. A preprocessing device for height measurement data for predicting growth, which is a preprocessing device for height measurement data for predicting growth, wherein: include: A first input unit, for inputting time series biological component data of a subject; A second input unit, inputting identification data of the subject; A height data extraction unit, extracting height data from the biological component data; a decrease interval determination unit, used for determining whether there is an interval in which the height data decreases within the input interval; a growth stage classification unit, which, when there is a decrease interval of the height data, classifies the growth stage including the decrease interval; and The error determination unit determines an error in the height data based on a growth stage including the decrease interval.

14. The preprocessing device for height measurement data for predicting growth according to claim 13, wherein: The method further includes a connection unit for connecting the data inputted through the first input unit and the second input unit to generate first data.

15. The preprocessing device for height measurement data for predicting growth according to claim 14, wherein: The method further includes an error detection unit that deletes the biological component data when a preset value is measured in the biological component data in the first data.

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