Method for preprocessing height measurement data for predicting growth

Abstract

The present invention relates to a method, a device, and a computer program for preprocessing input data for predicting the growth of a child or adolescent. The method for preprocessing height measurement data for predicting growth according to an exemplary embodiment of the present invention comprises the steps of: receiving time-series biological component data on a subject; receiving identification data on the subject; extracting height data from the biological component data; determining whether an interval of decrease is present in an interval in which the height data has been input; and, when an interval of decrease is present in the height data, determining an error in the height data on the basis of the growth phase in which the interval of decrease is included.

Description

Preprocessing method of height measurement data for growth prediction

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

[0002] With the recent development of artificial intelligence technology, it is being applied to various fields, and methods for extracting features inherent in data through neural network models and generating additional information are being developed and used as replacements for existing data processing methods.

[0003] Current artificial intelligence technology is being applied beyond simply tracking and detecting objects, to learning past history and predicting the future, or to deriving current features that reflect time-series changes.

[0004] Furthermore, in AI, which learns from data, data quality is known to be a key factor in AI performance. For example, leading companies with the most advanced AI-based autonomous driving technologies emphasize the importance of securing high-quality data.

[0005] Among these, predictive analytics is a statistical and data mining technique that extracts information from data and uses it to predict trends and behavioral patterns. This predictive analytics can be applied to any area requiring decision-making based on information gleaned from data. The core of predictive analytics is understanding the relationships between variables and then predicting unknown variables.

[0006] For this purpose, various approaches are being used depending on the data characteristics and prediction target.

[0007] Among the various fields requiring predictive analysis is the physical growth of children and adolescents. Parents and adolescents alike are deeply interested in the timing and extent of height growth.

[0008] Conventional predictions of height growth suggest methods for predicting growth plates by X-ray or analyzing the relationship with genetic / environmental factors (Patent Publication No. 10-2075743, Patent Publication No. 10-1866208), or for converting the body data of sample subjects with different measurement periods or measurement counts into a form suitable for learning a growth prediction model (Patent Publication No. 10-2198302).

[0009] Since children and adolescents have different growth stages with different characteristics, taking this into account can increase the reliability of solutions provided through predicted data and analysis.

[0010] In particular, in the case of children and adolescents, since this includes a period of rapid physical changes according to the growth stage, it is necessary to first determine the errors in the input (or measurement) data (biocomponent data) to secure high-quality data to improve the accuracy of growth prediction and the performance of artificial intelligence learning.

[0011] Therefore, the present invention aims to solve the above-described problem.

[0012] One of the various tasks of the present invention is to provide a method, device and computer program for preprocessing input data for predicting the growth of children or adolescents and providing customized solutions for each stage of growth.

[0013] Various embodiments for solving the problem of the present invention can provide a preprocessing method of height measurement data for growth prediction performed by a computing device, the method including the steps of receiving time-series bio-component data of a subject, receiving identification data of the subject, extracting height data from the bio-component data, determining whether a decreasing section exists within a section in which the height data is input, and, if a decreasing section of the height data exists, determining an error in the height data based on a growth stage in which the decreasing section is included.

[0014] It may be characterized by further including a step of generating first data by connecting the time-series biological component data of the subject and the identification data of the subject.

[0015] After generating the first data, the method may further include a step of deleting the biocomponent data if a preset value is measured from the biocomponent data.

[0016] The above growth stages may include a normal growth phase, a rapid growth phase, a decelerated growth phase, and a growth plate phase, and may be characterized by being classified based on the monthly age of the identification data.

[0017] In the step of determining the above error, if the reduction section is included in the rapid growth period, it may be characterized in that the growth data of the reduction section is deleted.

[0018] The step of determining the above error may include, if the reduction section is included in the general growth period, calculating the difference between the height data of any first point forming the reduction section and the height data of a second point and the average data value at the corresponding monthly age of a plurality of sample subjects.

[0019] The step of determining the above error may further include a step of comparing the difference value calculated at the first point with the difference value calculated at the second point.

[0020] In the step of determining the above error, if the difference value calculated at the first point is smaller than the difference value calculated at the second point, the extension data of the second point may be deleted.

[0021] In the step of determining the above error, if the difference value calculated at the first point is greater than or equal to the difference value calculated at the second point, the extension data of the first point may be deleted.

[0022] In the step of determining the above error, if the reduction section is included in the deceleration growth period or the no-growth period, it may be characterized in that the data of the reduction section is deleted based on the period of the reduction section and the degree of reduction in height.

[0023] An exemplary embodiment of the present invention can provide a program stored on a computer-readable recording medium including a program code for executing the above-described preprocessing method of height measurement data.

[0024] An exemplary embodiment of the present invention may provide a computer-readable recording medium having recorded thereon a program for executing the above-described method for preprocessing height measurement data for growth prediction.

[0025] An exemplary embodiment of the present invention provides a preprocessing device for height measurement data for growth prediction, comprising: a first input unit for receiving time-series biocomponent data of a subject; a second input unit for receiving identification data of the subject; a height data extraction unit for extracting height data from the biocomponent data; a decrease section determination unit for determining whether a decrease section exists within the input section of the height data; a growth stage classification unit for classifying a growth stage including the decrease section when the decrease section of the height data exists; and an error determination unit for determining an error in the height data based on the growth stage including the decrease section.

[0026] It may be characterized by further including a connecting unit that connects data input through the first input unit and the second input unit to generate first data.

[0027] The method may further include an error detection unit that deletes the biocomponent data when a preset value is measured from the biocomponent data among the first data.

[0028] Each feature of the above-described embodiments may be implemented in combination in other embodiments as long as it is not inconsistent with or exclusive of other embodiments.

[0029] According to various embodiments of the present invention, when predicting the growth of a child or adolescent, the accuracy of growth prediction can be improved by determining errors in bio-component data measured for predicting the growth of the child or adolescent.

[0030] According to various embodiments of the present invention, the accuracy of growth prediction can be improved by determining the interval in which height loss occurs and eliminating potential errors in height measurement. Furthermore, by considering the growth stage of children or adolescents when determining errors in the interval in which height loss occurs, error data can be more effectively determined.

[0031] According to various embodiments of the present invention, when providing a solution necessary for the growth of a child or adolescent, the accuracy of growth prediction can be improved by eliminating errors in input data.

[0032] According to various embodiments of the present invention, the accuracy of growth prediction of children or adolescents can be improved by determining errors in measured biocomponent data for growth prediction of children or adolescents and generating refined input data.

[0033] According to various embodiments of the present invention, the accuracy of solutions required for children or adolescents can be improved by determining errors in measured bio-component data to generate refined input data for generating solutions required for the growth of children or adolescents.

[0034] The effects of the present invention are not limited to those described above, and other effects not mentioned will be clearly recognized by those skilled in the art from the description below.

[0035] FIG. 1 is a diagram showing the configuration of a growth prediction and solution generation device according to an exemplary embodiment of the present invention.

[0036] Figure 2 is a diagram showing the data preprocessing unit of Figure 1.

[0037] FIG. 3 is a diagram showing predicted height and target height for each growth stage according to an exemplary embodiment of the present invention.

[0038] Figure 4 is a diagram showing growth in obesity and normal weight at each growth stage of a male according to an exemplary embodiment of the present invention.

[0039] FIG. 5 is a diagram showing the growth stage of a girl in obesity and normal weight according to an exemplary embodiment of the present invention.

[0040] FIG. 6 is a diagram illustrating the configuration of a neural network that performs growth prediction and solution provision according to an exemplary embodiment of the present invention.

[0041] FIG. 7 is a diagram illustrating a first neural network model according to an exemplary embodiment of the present invention.

[0042] FIG. 8 is a diagram illustrating a second neural network model according to an exemplary embodiment of the present invention.

[0043] FIGS. 9 to 11 are diagrams illustrating a preprocessing method of height measurement data for growth prediction according to an exemplary embodiment of the present invention.

[0044] Figures 12 and 13 are diagrams visually illustrating a preprocessing process of input data according to an exemplary embodiment of the present invention.

[0045] Figure 14 is a diagram showing biocomponent data of a subject according to an exemplary embodiment of the present invention.

[0046] FIG. 15 is a diagram showing data linking a subject's biocomponent data and identification data according to an exemplary embodiment of the present invention.

[0047] 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, devices, and / or systems described herein. However, these are merely examples and the present invention is not limited thereto.

[0048] In describing embodiments of the present invention, detailed descriptions of known technologies related to the present invention will be omitted if they are deemed to unnecessarily obscure the gist of the invention. Furthermore, the terms described below are defined based on their functions in the present invention and may vary depending on the intent or custom of the user or operator. Therefore, their definitions should be based on the overall content of this specification.

[0049] The terminology used in this detailed description is for the purpose of describing embodiments of the present invention only and should not be construed as limiting. Unless expressly stated otherwise, singular forms include plural forms.

[0050] In this description, expressions such as "including" or "having" are intended to indicate certain features, numbers, steps, operations, elements, portions or combinations thereof, and should not be construed to exclude the presence or possibility of one or more other features, numbers, steps, operations, elements, portions or combinations thereof other than those described.

[0051] Additionally, in describing components of embodiments of the present invention, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only intended to distinguish the components from other components, and the nature, order, or sequence of the components are not limited by the terms.

[0052] FIG. 1 is a diagram showing the configuration of a growth prediction and solution generation device according to an exemplary embodiment of the present invention, FIG. 2 is a diagram showing a data preprocessing unit of FIG. 1, FIG. 14 is a diagram showing biocomponent data of a subject according to an exemplary embodiment of the present invention, and FIG. 15 is a diagram showing data linking biocomponent data and identification data of a subject according to an exemplary embodiment of the present invention.

[0053] The following description is provided with reference to FIGS. 1, 2, 14, and 15.

[0054] The growth prediction system of this embodiment can preprocess and then receive the subject's time-series physical data. Based on the input physical data, the system can distinguish the subject's gender and generate growth predictions or solutions based on growth predictions using different growth prediction models depending on the subject's gender.

[0055] Boys and girls may experience different growth rates at different stages of development. For example, as described below, boys and girls may enter rapid growth stages at different times. Therefore, the rate of growth and the resulting solutions that take this growth rate into account may differ. Therefore, it is desirable to perform growth predictions using models trained with different training data based on the subject's gender.

[0056] More specifically, a 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 determination unit (20), a growth stage determination unit (30), a prediction unit (50), a solution generation unit (70), and a display unit (90).

[0057] Through the input unit (10), the growth prediction and solution generation device can receive time-series physical data on the subject.

[0058] The subject's physical data may include data that can identify the subject (identification data) and the subject's biometric data. The identification data and biometric data may be composed of time-series data.

[0059] For example, the above identification data may include data for identifying a subject, such as grade (or age), gender, and height, and the above biocomponent data may include data such as weight, protein, mineral content, body fat, body water, muscle mass, body fat mass, bone tissue, skeletal muscle mass, body mass index (BMI), basal metabolic rate, neck circumference, chest circumference, abdominal circumference, thigh circumference, arm circumference, and hip circumference.

[0060] More specifically, referring to FIGS. 14 and 15, the subject's identification data (1201) may include date of birth (1201-1), gender (1201-2), age (1201-3), and measurement date (1201-4).

[0061] In addition, the subject's biological composition data (1101) may include data number (1101-1), height (1101-2), weight (1101-3), protein mass (1101-4), mineral mass (1101-4), 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).

[0062] This body data is only an example to help understand the present invention, and the present embodiment is not limited thereto, and it is obvious that the types of data constituting the body data may be varied depending on the embodiment.

[0063] Meanwhile, an exemplary embodiment of the present invention may include a data preprocessing unit (100) that determines errors in the subject's body data and generates refined data.

[0064] Since biological component data (1101) may undergo rapid changes depending on the physical characteristics of children or adolescents depending on the growth stage, errors (errors during the measurement process) may occur in other biological component data measurement processes such as the measurement method, measurement time, and measurement environment.

[0065] Additionally, errors (data transmission errors) may occur during the process of transmitting or storing measured biocomponent data.

[0066] Accordingly, the data preprocessing unit (100) of the present embodiment determines whether there is a section in which height (1101-2) decreases in the measured biocomponent data (1101), and if there is a section in which height data decreases, it can determine an error in the height data (110-2) by considering the growth stage of the subject through the monthly age data (1201-3) in the identification data (1201).

[0067] Refined data can be generated through a process of determining errors in the measurement process and data transmission when a reduction section occurs, particularly in the height data (1101-2) among the biocomponent data (1101) described above.

[0068] The data preprocessing unit (100) of the present embodiment can determine errors in the input biocomponent data (1101) and identification data (1201) as described above and generate refined data (second data). The second data thus generated can be used as input data for the input unit (10) to predict the growth of a subject or generate a solution through growth prediction.

[0069] Meanwhile, the data preprocessing unit (100) may include a biocomponent 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 section determination unit (153), a growth stage classification unit (155), a data deletion unit (180), and a second data generation unit (190).

[0070] The data preprocessing unit (100) can receive the subject's biocomponent data (1101) through the biocomponent data input unit (110), and can receive the subject's identification data (1201) through the identification data input unit (120).

[0071] The error detection unit (130) can detect whether a preset value exists among the input subject's biocomponent data (1101), and the preset value can be set to a 'Nan' value, which is an example of a measurement error.

[0072] The connection unit (140) can connect the subject's biocomponent data (1101) and identification data (1201) to generate first data (140d). 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).

[0073] The height data extraction unit (151) can extract height data (1101-2) from the input subject's biocomponent data (1101), and can determine whether a reduction section exists in the extracted height data through the reduction section determination unit (153).

[0074] The growth stage classification unit (155) can classify the growth stage of the subject based on the input subject identification data (1201).

[0075] The error determination unit (170) can determine an error in the height data if it is determined that a reduction section exists in the height data extracted through the reduction section determination unit (153).

[0076] More specifically, the error in the height data can be determined by setting a different standard for error judgment based on the growth stage that includes the reduction section of the height data based on the growth stage of the subject classified through the growth stage classification unit (155) described above.

[0077] The data deletion unit (180) can delete data determined to be an error through the error determination unit (170), and the second data generation unit (190) can determine an error in the input data and then generate refined data (second data).

[0078] The preprocessing method of height measurement data for growth prediction through the data preprocessing unit described above is described in more detail with reference to FIGS. 9 to 11.

[0079] Meanwhile, the data input into the input unit (10) may include the subject's time series body data.

[0080] The above time series body data may be continuous data or discontinuous data, but may be data included in at least one period corresponding to the growth stage of a child or adolescent.

[0081] More specifically, the timing and frequency of collection of the subject's time-series body data may vary.

[0082] For example, the first subject may have measured body data from ages 8 to 12, which is part of the childhood or adolescent period, and the second subject may have irregularly measured body data, such as ages 8, 10 to 12, and 15.

[0083] Additionally, the third subject may have body data measured multiple times over a period of time (a period corresponding to one of the growth stages of a child or adolescent), while the fourth subject may have body data measured only once over a period of time (a period corresponding to one of the growth stages of a child or adolescent).

[0084] As described above, depending on the collection period and number of collections, the subject's physical data may be included in more than one of the growth stages of a child or adolescent (subject 1, subject 2), but may not be included (subject 3, subject 4).

[0085] In the case where there is body data measured multiple times during one of multiple growth stages, such as the third subject, the growth stage determination unit (30) can classify the growth stage including the body data of the third subject through the growth stage classification unit (31) and extract the body data through the body data extraction unit (33). The body data extracted here can generally include all of the body data of the third subject input through the input unit (10).

[0086] However, in cases where the subject's body data, such as that of the fourth subject, corresponds to only one of multiple growth stages and there is body data measured only once during that period, additional body data corresponding to an arbitrary period may be generated before entering the body data of the fourth subject into the growth stage determination unit (30).

[0087] More specifically, the time-series body data of the subject corresponding to an arbitrary period can be generated based on the time-series body growth data of a plurality of previously stored sample subjects based on the body data of the input subject.

[0088] For example, body data can be generated based on a distribution model (similarity) between the body data of the input subject and the time-series body growth data of multiple previously stored sample subjects, or body data can be generated based on a Bayesian inference model (conditional probability).

[0089] The growth stage determination unit (30) can classify the subject's body data input through the input unit (10) into one of a plurality of growth stages through the growth stage classification unit (31) and extract body data corresponding to the classified growth stage through the body data extraction unit (33).

[0090]

[0091] FIG. 3 is a diagram showing predicted height and target height for each growth stage according to an exemplary embodiment of the present invention.

[0092] In Figure 3, the x-axis represents age (months), and the y-axis represents height (cm). The lower dotted line (P) represents the subject's predicted growth rate, and the upper solid line (G) represents the subject's target growth rate.

[0093] Referring to Figure 3, the growth stages of children or adolescents described above may include a normal growth period (301), a rapid growth period (303), a decelerated growth period (305), and a growth plate period (307).

[0094] Each growth stage can be distinguished by its growth rate, and the height that grows each year varies depending on the growth stage, and even in the same growth stage, the actual height that grows can vary depending on the growth type.

[0095] The normal growth period (301) usually refers to the period before puberty when secondary sexual characteristics appear, and children or adolescents in this period generally have open growth plates, so they generally grow 4 to 5 cm per year in the case of a short growth type and 6 to 7 cm per year in the case of a tall growth type, depending on the growth environment.

[0096] The rapid growth period (303) is the period when secondary sexual characteristics begin to appear. In women, the breasts swell and pubic hair develops, and in men, the testicles enlarge and pubic hair begins to grow, and the voice changes to a voice stage. The rapid growth period (303) generally lasts about 2 to 3 years after the normal growth period (301), and growth occurs at an average rate of about 7 to 10 cm per year.

[0097] The deceleration period (305) refers to the period when secondary sexual characteristics are fully developed. During this period, women can be distinguished by the onset of menstruation, and men can clearly see the changes in pubic hair, voice change, and armpit hair. When the deceleration period (305) begins, the growth rate rapidly decreases compared to the rapid growth period (303), and it generally lasts for about 2 to 3 years, and natural growth stops after growing at an average of 5 to 6 cm per year. The growth plate begins to close little by little after the rapid growth period (304), and about 50% of it is closed about 6 months after entering the deceleration period (305).

[0098] The growth plate period (307) is a period when the growth period is not completely over, but natural height growth becomes difficult and the growth plate is closed. Generally, women enter the growth plate period (307) about 1 year and 6 months to 2 years after menarche, and men enter the growth plate period (307) about 1 year and 6 months to 2 years after the time when hair begins to grow in the armpits. During the growth plate period (307), the growth plate closes and natural growth stops, but it is possible to grow in the range of about 1 to 3 cm by changing bad lifestyle habits and improving physical functions through customized exercise, posture correction, and nutrient intake.

[0099] Meanwhile, the prediction unit (50) can be implemented as an artificial intelligence with a recursive neural network (RNN) structure as a prediction model so that it can utilize not only the current value but also time-series values. For example, the prediction model can be implemented with an architecture such as a recurrent neural network such as LSTM (Long Short Term Memory) or GRU (Gated Recurrent Units). Of course, various conventional artificial intelligence architectures can also be applied to the prediction model of the present embodiment, and will be described in detail with reference to FIGS. 6 to 8 described below.

[0100] The solution generation unit (70) can generate a growth management solution based on the subject's body data corresponding to the classified growth stage.

[0101] More specifically, when the subject is in the general growth stage (301), a solution for increasing the subject's growth prediction value can be provided. The growth prediction value is a value corresponding to the y-axis in FIG. 4, and the solution for increasing the growth prediction value can be provided to the subject through various solution display units (90) for increasing the target value of the predicted y-axis.

[0102] Meanwhile, examples of solutions provided through the display unit (90) may include current height, predicted height, obesity level, body fat mass, skeletal muscle mass, protein mass, mineral mass, sleep amount, exercise amount, nutritional information, lifestyle habits, posture, etc. Each indicator may be expressed as caution, average, good, etc. based on a preset range, or may be expressed as a level.

[0103] And for each indicator, the current status, customized solutions, and precautions can be displayed. The current status can be displayed in stages or levels based on the target value, and in the case of customized solutions, it can include information on adjusting protein, total minerals, body fat, body water, soft lean mass, fat-free mass, bone tissue, skeletal muscle mass, body mass index (BMI), basal metabolic rate, etc. to reach the current target value based on the entered body data.

[0104] In the case of precautions, it may include information on adjusting the currently deficient protein, total minerals, body fat, body water, soft lean mass, fat free mass, bone tissue, skeletal muscle mass, body mass index (BMI), basal metabolic rate, etc. based on the entered body data.

[0105] In addition, when the subject is in the rapid growth stage (303), a solution for increasing the growth prediction value of the subject's rapid growth stage (303) can be provided. The growth prediction value is a value corresponding to the y-axis in FIG. 2, and the solution for increasing the growth prediction value can provide the subject with various solutions for increasing the target value of the predicted y-axis through the display unit (90).

[0106]

[0107] FIG. 4 is a diagram showing growth in obesity and normal weight at each growth stage of a boy according to an exemplary embodiment of the present invention, and FIG. 5 is a diagram showing growth in obesity and normal weight at each growth stage of a girl according to an exemplary embodiment of the present invention.

[0108] The x-axis of Figure 4 represents age, and the y-axis represents growth rate (cm). The solid line (G1) represents the growth rate by growth stage for a normal-weight male, and the dotted line (P1) represents the growth rate by growth stage for an obese male.

[0109] The x-axis of Figure 5 represents age, and the y-axis represents growth rate (cm). The solid line (G2) represents the growth rate by growth stage for normal weight girls, and the dotted line (P2) represents the growth rate by growth stage for obese girls.

[0110] The following description is provided with reference to Figures 4 and 5.

[0111] Obesity is not simply a simple increase in body weight. It is a disease characterized by excessive accumulation of fat tissue, often accompanied by metabolic disorders. Childhood or adolescent obesity is medically defined as a weight exceeding 20% ​​of the recommended weight for height, typically between infancy and adolescence.

[0112] In most cases, childhood obesity disappears after the first birthday as children become more active and mobile. However, some children remain obese, and in many cases, the condition returns to normal only to relapse during school age.

[0113] Between 75% and 80% of childhood and adolescent obesity progresses to adult obesity. Furthermore, it inhibits growth hormone secretion, which, especially in girls, accelerates puberty and shortens the growth period, hindering growth and causing precocious puberty.

[0114] Therefore, for school-age children and adolescents who are prone to obesity, it is necessary to provide a systematic solution to predict obesity and prevent it if it is predicted.

[0115] Meanwhile, childhood or adolescent obesity can be divided into simple obesity with no clear cause and symptomatic obesity caused by a specific cause disease, and more than 99% of childhood obesity is simple obesity.

[0116] Children with simple obesity are generally characterized by being average or slightly taller than their peers of the same age (multiple sample subjects) during the normal growth period (301) for both boys and girls, but after the rapid growth period (303), they tend to be shorter or have a lower growth rate than their peers of the same age (multiple sample subjects).

[0117] In summary, childhood or adolescent obesity can be understood as a group of diseases that are accompanied by overweight or metabolic disorders caused by a wide variety of causes, and referring to Figures 5 and 6, in general, both boys and girls show an average or slightly higher growth rate than multiple sample subjects of the same age during the normal growth period (301), but after the rapid growth period (303), they show a tendency for a lower growth rate than multiple sample subjects of the same age.

[0118] Therefore, in order to more accurately predict obesity and generate a solution based on growth stage, the present embodiment classifies the gender of the subject through the gender determination unit (20) based on the physical data of the subject input through the input unit (10), and then classifies the growth stage based on the classified gender in the growth stage determination unit (30), extracts the physical data, and then generates a solution in the solution generation unit (70) by considering the gender and growth stage of the subject.

[0119] More specifically, in cases where both boys and girls are commonly obese, it can be confirmed that the growth prediction value in the rapid growth stage (303) is lower than in the normal case. Therefore, if the subject is in the rapid growth stage (303), a solution for increasing the growth prediction value can be provided, and if the subject is in the normal growth stage (301), the content can include information on adjusting indicators that can alleviate cases such as cases where sex hormones increase abnormally, including the physical data considered.

[0120] In addition, when the subject is in the decelerated growth phase (305), a solution for adjusting the duration of the subject's decelerated growth phase (305) can be provided. The growth phase duration adjustment can be divided into a case where the subject's body data is located at the beginning of the decelerated growth phase (305) and a case where the subject's body data is located in the middle or latter part of the decelerated growth phase (305) among the growth stages classified based on the input body data of the subject.

[0121] The criteria for distinguishing between the beginning and the middle and late stages of the aforementioned deceleration growth stage (305) may be distinguished based on a predetermined range corresponding to the rapid growth stage (303) to the deceleration growth stage (305) based on the x-axis in FIGS. 4 to 6, or based on the body data of the input subject, if the secondary sexual characteristics are not completed, it may be distinguished as the beginning of the deceleration growth stage (305), and if completed, it may be distinguished as the middle and late stages of the deceleration growth stage (305).

[0122] Preferably, it is possible to determine whether the secondary sexual characteristics have been completed based on the input body data of the subject, and thus determine whether the body data of the current subject is in the beginning or the middle or latter part of the decelerated growth phase (305). In a case where it is not possible to determine whether the secondary sexual characteristics have been completed based on the input body data of the subject, it is possible to determine whether the body data of the subject is in the beginning or the middle or latter part of the decelerated growth phase (305) based on a predetermined range corresponding to the decelerated growth phase (305) from the rapid growth phase (303).

[0123] Meanwhile, if the body information of the input subject is located at the beginning of the deceleration growth phase (305), a period adjustment solution can be provided to delay entry into the deceleration growth phase (305).

[0124] As described above, since secondary sexual characteristics are being completed at the point of transition from the rapid growth phase (303) to the decelerated growth phase (305), a solution for delaying the point of completion of secondary growth can be provided, and various solutions for moving the range of the x-axis corresponding to the decelerated growth phase (305) to the right in FIGS. 4 to 6 can be provided to the subject through the display unit (90). In this case, the range of the decelerated growth phase (305) may increase depending on the subject's physical data, or may decrease as the rapid growth phase (303) increases.

[0125] And, if the body data of the input subject is located in the middle or latter part of the deceleration growth phase (305), a period adjustment solution for increasing the period of the deceleration growth phase (305) can be provided.

[0126] As described above, the decelerated growth phase (305) refers to the time when the subject's growth plate closes. Generally, after entering the decelerated growth phase (305), about 50% of the growth plate closes after 6 months. When the growth plate closes and natural growth stops, the no-growth phase (307) is entered. Therefore, in this case, a solution for increasing the period of the decelerated growth phase (305) can be provided. In other words, various solutions for expanding the range of the x-axis corresponding to the decelerated growth phase (305) in FIGS. 4 to 6 can be provided to the subject through the display unit (90).

[0127] If the subject described above is in the general growth stage (301), the physical data to be considered may include, in particular, content on the adjustment of indicators that can alleviate the degree of closure of the growth plate.

[0128] In addition, if the subject is in the growth plate stage (307), a solution for improving physical function through lifestyle habits, customized exercise, posture correction, nutrient intake, etc. can be provided based on the subject's entered physical data.

[0129] In the case of the growth plate stage (307), the growth plate closes and natural growth stops, so a solution for improving physical function through lifestyle habits, customized exercise, posture correction, etc. 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 a solution for improving physical function through nutrient intake, etc. can be provided based on protein, mineral content, bone tissue (bone density), etc.

[0130] Meanwhile, in order to provide a more accurate obesity prediction and solution in this embodiment, the gender of the subject is classified through the gender determination unit (20), and the growth stage classification unit (31) can set the timing of the rapid growth stage differently based on the classified gender.

[0131] This is because, as described above, the timing of entering the rapid growth phase may differ for boys and girls. Furthermore, the subject's body data corresponding to the rapid growth phase, taking into account gender and output from the body data extraction unit (33), is input into the prediction unit (50) to output a predicted value for obesity.

[0132] If the subject is predicted to be obese and is male, a solution for increasing the predicted growth value of the subject in the rapid growth phase (303) can be provided as described above.

[0133] And if the subject is predicted to be obese and is female, a solution for increasing the duration of the rapid growth phase (303) can be provided.

[0134] In more detail, referring to FIGS. 4 and 5, the difference in predicted growth values ​​in the rapid growth stage (303) is smaller for women compared to men, but the decrease in the degree of height growth (growth rate) for each age is large as they move from the rapid growth stage (303) to the decelerated growth stage (305).

[0135] For example, in Fig. 4, it can be seen that the difference in growth prediction values ​​in the rapid growth phase (303) for a normal-weight male is significantly different between the growth prediction values ​​in the normal-weight case (G1) and the obese case (P1). Therefore, for obese males, it is necessary to provide a solution for increasing the growth prediction value in the rapid growth phase (303).

[0136] Also, as an example, in Figure 5, a girl of normal weight grew 7 cm in height as she grew from age 9 to 10, and grew about 6.5 cm in height as she grew from age 10 to 11.

[0137] In contrast, obese females grew approximately 6.7 cm in height from age 9 to 10, and approximately 5.7 cm from age 10 to 11. Furthermore, as shown in Figure 5, the difference in predicted growth values ​​is relatively small compared to that of males.

[0138] Therefore, for women who are obese, it is necessary to provide a solution to increase the period of the rapid growth phase (303) and reduce the decrease in growth rate that occurs when moving from the rapid growth phase to the decelerated growth phase.

[0139]

[0140] FIG. 6 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. 7 is a diagram showing a first neural network model according to an exemplary embodiment of the present invention, and FIG. 8 is a diagram showing a second neural network model according to an exemplary embodiment of the present invention.

[0141] The following description is provided with reference to Figures 6 to 8.

[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 the 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 growth stage among a plurality of growth stages as learning data based on time-series body data for a plurality of sample subjects.

[0144] The above growth stage may be the rapid growth stage (303), which is the point at which growth retardation due to obesity occurs, but, as described above, it is of course possible to adopt one or more of the multiple growth stages to more accurately predict obesity.

[0145] The first model (50) is equipped with LSTM neural networks (50-1, 50-2, 50-3, 50-4) for learning time-series data, and trains the LSTM neural networks (50-1, 50-2, 50-3, 50-4) using past physical information of multiple sample subjects. Then, by inputting the current subject's physical data into the trained LSTM neural networks (50-1, 50-2, 50-3, 50-4), the predicted growth rate and a solution considering the growth rate can be output.

[0146] The above subject's body data may refer to refined data that has gone through an error judgment process through a data preprocessing unit (100), which will be described in more detail with reference to FIGS. 10 and 11 described below.

[0147] LSTM neural networks (50-1, 50-2, 50-3, 50-4) are trained using at least one of the body data of multiple sample subjects as a baseline. For example, for height, they are trained using annual height data from a random period or during a specific growth stage, and then predicted for the next year and compared with the actual data. This comparison allows the training set to learn by moving forward in time by a random period or by a specific growth stage.

[0148] Additionally, LSTM neural networks (50-1, 50-2, 50-3, 50-4) can be trained for each growth stage. Accordingly, they can be trained with past body data of the corresponding growth stages, such as the normal growth stage (301), rapid growth stage (303), decelerating growth stage (305), and growth plate stage (307).

[0149] The subject's body data for the above learning may mean refined data that has gone through an error judgment process through a data preprocessing unit (100), which will be described in more detail with reference to FIGS. 9 to 13 described below.

[0150] Meanwhile, as an example, in this embodiment, time-series physical data of multiple sample subjects can be sequentially input as learning data according to age or an arbitrary period, and the result of calculating the predicted value at a past point in time or growth rate can be transferred to predict the growth rate at the next age or an arbitrary period.

[0151] Therefore, the LSTM neural network (50-1, 50-2, 50-3, 50-4) can learn not only the growth prediction based on the current body data, but also the extent to which the prediction results for various indicators from the past (50-1, 50-2, 50-3, 50-4) affect the current growth prediction, and through this, it can extract items that have a large influence on the change in growth according to age or an arbitrary period among the indicators and reflect them in the growth prediction.

[0152] Furthermore, for time-series learning, it is necessary to secure regular, time-series physical data for multiple sample subjects. However, as described above, it can be difficult to regularly obtain physical data for multiple sample subjects, such as by age or over a given period. Therefore, outliers or discontinuous physical data can be removed for each unit period, allowing for temporal normalization.

[0153] Meanwhile, the second model (13) can derive bone maturity (age) from a bone image using a convolutional neural network trained with the subject's bone maturity data as learning data.

[0154] More specifically, the convolutional neural network includes multiple convolution layers that create a features map for features in the target image of the analysis among the bone images, and a pooling layer that performs sub-sampling between the multiple convolution layers, so that features at different levels for the target area of ​​analysis can be extracted, and the features can be probabilistically inferred through an activation function, or the bone maturity can be derived through weight learning between nodes through regression analysis.

[0155] The bone maturity extracted through the second model (13) can be input into the LSTM neural network (50-1, 50-2, 50-3, 50-4) together with at least some of the subject's body data to increase the accuracy of predicting the subject's growth, thereby further increasing the accuracy of predicting the subject's obesity, precocious puberty, growth, etc.

[0156]

[0157] FIGS. 9 to 11 are diagrams illustrating a preprocessing method of height measurement data for growth prediction according to an exemplary embodiment of the present invention, and FIGS. 12 and 13 are diagrams visually illustrating a preprocessing process of input data according to an exemplary embodiment of the present invention.

[0158] As described above, when predicting the growth of a subject (child or adolescent) or providing a solution through growth prediction, the entered physical data may have errors in the measurement process or data transmission.

[0159] Therefore, in order to provide a more accurate growth prediction of the subject or a solution through growth prediction, it is desirable to input refined data that has gone through the process of detecting and judging the above-described error data into the input unit (10).

[0160] In addition, the body data of multiple sample subjects for training the prediction model of the prediction unit (50) described above may also be refined data that has gone through the process of detecting and judging the error data described above.

[0161] The process of detecting and judging errors in height measurement data is described below with reference to FIGS. 2, 9 to 13.

[0162] An exemplary embodiment of the present invention can input body data (S10) to detect or determine an error in a subject's body data, particularly height data, and then extract height data from the input body data (S30).

[0163] And, based on the extracted height data, it is determined whether there is a height reduction section (S50), and if there is a height reduction section (S50: Yes), the growth stage of the section where the height data decreases can be classified (S70). Therefore, through the above step (S70), it is possible to classify whether the section where the height decreases corresponds to any one of the general growth period (301), rapid growth period (303), decelerating growth period (305), and no growth plate period (307) described above.

[0164] And, considering the growth stage, errors in body data can be judged (S90).

[0165] More specifically, the subject's body data may include identification data (1201) and biocomponent data (1101) as shown in FIG. 15, and the preprocessing unit (100) may input (S111) the subject's biocomponent data (1101) through the biocomponent data input unit (110), and may input (S112) the subject's identification data (1201) through the identification data input unit (120).

[0166] And the input biocomponent data (1101) and identification data (1201) can be generated as data (140d) concatenated through a connecting unit (140). The data (140d) can also be named first data to distinguish it from the second data refined through the data preprocessing unit (100) of the present embodiment.

[0167] If it is determined that an error has been detected in the input bio-component data (1101) through the process of detecting an error in the bio-component data (S131) ​​through the error detection unit (130) of the connected data (140d) (S131: Yes), at least a portion of the bio-component data constituting the connected data (140d) may be deleted (S132).

[0168] For example, if at least one of the multiple items (1101-1 to 1101-9) constituting the input bio-component data (1101) includes a 'Nan' value, the data preprocessing unit (100) of the present embodiment can delete the bio-component data (1101) through the data deletion unit (180). In this case, all items measured on the measurement date including the 'Nan' value (rows including the measurement date) based on the measurement date (1201-4) of the identification data can be deleted.

[0169] Alternatively, if the age of the identification data (1201-3) is 30 months or less, all items measured on the measurement date that include the 'Nan' value (rows including the measurement date) may be deleted. In more detail, if the age is 30 months or less, it is a period of rapid growth in infants and toddlers, and many errors may be included in the measurement, so the reliability of the biocomponent data is relatively lower than that of biocomponent data with a higher age. Therefore, if the biocomponent data includes the 'Nan' value as described above when the age is 30 months or less, all items measured on the measurement date that includes the 'Nan' value may be deleted.

[0170] The deletion of the bio-component data described above is exemplary, and if an error (e.g., a Nan value) is included among the items constituting the bio-component data based on various criteria, it can be detected and deleted.

[0171] Meanwhile, if no error is detected in the biocomponent data (1101) (S131: No), the kidney data (1101-2) can be extracted through the kidney data extraction unit (151).

[0172] And, through the reduction section determination unit (153), it can be determined (S511) whether a reduction section exists in the extracted height data. If there is no reduction section in the height data (S511: No), since there is no error in the height data, refined second data that can be input to the input unit (10) or the prediction model can be generated (S5112) through the second data generation unit (190).

[0173] Meanwhile, if there is a reduction section of the height data (S511: Yes), the growth stage including the reduction section of the height data can be classified (S711) through the growth stage classification unit (155), and an error in the biocomponent data can be determined.

[0174] More specifically, the growth rate of children and adolescents differs at each growth stage, and thus the extent of height change varies. For example, the extent of height change during the rapid growth stage (303) is generally greater than the extent of height change during the normal growth stage (301).

[0175] Therefore, when determining an error in kidney data, different criteria can be applied when the decrease section of kidney data corresponds to the rapid growth phase (303) and when the decrease section of heart data corresponds to the normal growth phase (301).

[0176] More specifically, the present embodiment can classify a growth stage including a decreasing section of kidney data (S711) and then determine whether the decreasing section is included in a rapid growth period (S713).

[0177] If the decreasing section is included in the rapid growth period (S713: Yes), the data deletion unit (180) can delete all growth data forming the decreasing section (S7131) and generate refined second data through the second data generation unit (190).

[0178] Data deleted through the data deletion unit (180) may refer to kidney data (1101-2) among the biocomponent data forming the reduction section of kidney data.

[0179] Alternatively, to increase the reliability of the data, all items measured on a measurement date (rows containing the measurement date) that contain height data forming a decreasing interval of height data based on the measurement date (1201-4) of the identification data may be deleted. In other words, all data in rows containing height data forming a decreasing interval of height data may be deleted.

[0180] Meanwhile, if the decreasing section is not included in the rapid growth period (S713: No), it can be determined whether the decreasing section is included in the normal growth period (S715).

[0181] The data error judgment criteria when the decreasing section is included in the normal growth period (S715: Yes) may be set differently from the data error judgment criteria when the decreasing section is included in the rapid growth period (S713: Yes).

[0182] As described above, since the range of height change in the rapid growth phase (303) is greater than the range of height change in the normal growth phase (301), if a section in which the continuously measured height decreases occurs in the rapid growth phase (303), all height data forming the decrease section can be deleted, and if a section in which the continuously measured height decreases occurs in the normal growth phase (301), data among the height data forming the decrease section that is more likely to be erroneous data can be deleted.

[0183] More specifically, if the decreasing section is included in the general growth period (S715: Yes), the error determination unit (170) can calculate the difference value between the height at the first point forming the decreasing section and the height at the second point and the average of multiple sample subjects. The first point and the second point may represent the starting point and the ending point of the decreasing section.

[0184] In the above step (S7151), the difference value at the first point and the difference value at the second point are compared (S7153). If the difference value at the first point is greater than the difference value at the second point (S7153: Yes), the height data at the first point can be deleted (S7154) and then the second data can be generated (S7155). If the difference value at the first point is less than or equal to the difference value at the second point (S7153: No), the height data at the second point can be deleted (S7156) and then the second data can be generated (S7157).

[0185] The reason why the difference value at the second point is deleted when the difference value at the first point and the difference value at the second point are the same in the above step (S7153) is that the elongation data at the second point is measured later in time than the elongation data at the first point, so it is closer to the rapid growth phase (303), and therefore the probability that the elongation data measured at the second point is erroneous is higher.

[0186] Figures 12 and 13 are diagrams visually illustrating the preprocessing process of data when the decreasing section of height data is included in the general growth stage. In Figures 12 and 13, the x-axis represents age in months (1201-3), the y-axis represents height (1101-2, cm), the dotted lines (r1, r2) represent the height data of the subject, and the solid line (e) in Figure 12 represents data obtained by fitting the body data (growth curves) of multiple sample subjects to a specific function.

[0187] In Fig. 12, m2 represents the starting point (first point) of the decreasing section of the height data, and m3 represents the ending point (second point) of the decreasing section of the height data. The data preprocessing unit (100) of the present embodiment can calculate the difference value from the average of multiple sample subjects at each point (m2, m3) when the decreasing section (m2, m3) is included in the general growth stage, as shown in Fig. 12.

[0188] The above difference value can be calculated as the difference between the height data of the subject at the first point (m2) and the height data of the subject at the second point (m3) and the average of the height data of multiple sample subjects as described above, and can also be calculated as the difference between the data (e) obtained by fitting the body data (growth curve) of multiple sample subjects to a specific function as shown in Fig. 12.

[0189] The body data of the above-described plurality of sample subjects may be stored in the error determination unit (170), and a calculation of the difference between the height data of the subject and the height data of the plurality of sample subjects may be performed. Alternatively, the body data of the above-described plurality of sample subjects may be fitted with a specific function, and then a calculation of the difference between the height data of the subject and the height data of the plurality of sample subjects may be performed. Of course, it is obvious that the above-described calculation may also be performed through a separate calculation unit (not shown) that may be included in the data preprocessing unit (100).

[0190] In this example, the data preprocessing unit (100) can delete the height data of the first point (m2) through the data deletion unit (180) because the difference value calculated at the first point (m2) is greater than the difference value calculated at the second point (m3) (S7153: No), and then generate refined second data through the second data generation unit (190).

[0191] Meanwhile, if the decreasing section is not included in the normal growth phase (S715: No), the error judgment unit (170) can compare the period of the decreasing section with the preset first period (S731). The step (S731) corresponds to a case where the decreasing section present in the height data (1101-2) input through the above-described step is not included in the normal growth phase (301) or the rapid growth phase (303), and thus corresponds to either the deceleration growth phase (305) or the no-growth phase (307).

[0192] Accordingly, in the case described above, the present embodiment can delete data in the reduction section based on the period of the reduction section and the degree of reduction in the extension data.

[0193] More specifically, if the duration of the reduction section is less than the preset first duration (S731: Yes), the error determination unit (170) can calculate the height difference value between the start point and the end point of the reduction section (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 value).

[0194] And, a step of comparing the height difference value with a preset first reference value (S7313) can be performed.

[0195] If the height difference value is greater than or equal to the preset first reference value (S7314: Yes), this means that the decrease in height is greater than the first reference value within a relatively short period of time (the period of the decrease section <less than the first period), and therefore, the data preprocessing unit (100) can delete the height data of the decrease section through the data deletion unit (180) (S7314) and generate second data through the second data generation unit (190) (S7315).

[0196] The height data deleted in the above step (S7314) may mean height data corresponding to the start point of the reduction section and height data corresponding to the end point.

[0197] Alternatively, the height data deleted in the above step (S7314) may mean either the height data corresponding to the starting point of the reduction section or the height data corresponding to the ending point. In this case, as described above, the height data at each point may be compared with multiple sample subjects and then selectively deleted based on the difference value.

[0198] In addition, as described above, the data deleted in the above step (S7314) may include not only the growth data corresponding to the start or end point of the reduction section, but also all data (rows) included in the measurement date on which the growth data was measured.

[0199] And if the height difference value is less than the preset first reference value (S7313: No), the data preprocessing unit (100) can generate second data (S7316) through the second data generation unit (190).

[0200] For example, the first period may be set to 6 months, and the first reference value may be set to 1 cm.

[0201] Meanwhile, if the period of the reduction section is longer than the preset first period (S731: No), the error determination unit (170) can determine whether the period of the reduction section is included within the range of the first period and the preset second period (S751).

[0202] If the above-mentioned reduction section is greater than or equal to the first period and less than the second period (S751: Yes), the error determination unit (170) can calculate the height difference value between the start point and the end point of the reduction section (S7511). That is, in the above-mentioned step (S7511), the error determination unit (170) derives the degree of reduction of the height data (hereinafter, the height difference value).

[0203] And, a step of comparing the height difference value with a preset second reference value (S7513) can be performed.

[0204] If the height difference value 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 reduction section through the data deletion unit (180) (S7514) and generate second data through the second data generation unit (190) (S7515).

[0205] And if the height difference value is less than the preset second reference value (S7513: No), the data preprocessing unit (100) can generate second data (S7516) through the second data generation unit (190).

[0206] For example, the second period may be set to 24 months, and the second reference value may be set to 0.5 cm.

[0207] In the case where the period of the reduction section of the kidney data is included in the range set as the first period and the second period in the above step (S751), it means that the difference in the number of measurement months of the input data is greater than when the reduction section is less than the first period. Therefore, as described above, it is preferable that the second criterion be set to a smaller value than the first criterion.

[0208] Meanwhile, if the subject's age is outside the range set for the first period and the second period (S751: No), since there is a relatively large difference in the number of months measured, the error determination unit (170) determines all height data forming the decreasing section as an error, deletes all of the height data through the data deletion unit (180) (S7531), and then generates second data through the second data generation unit (190) (S7533).

[0209] The present invention has been described so far, focusing on preferred embodiments thereof. All embodiments and conditional examples disclosed herein are intended to assist those skilled in the art in understanding the principles and concepts of the present invention. Those skilled in the art will appreciate that the present invention can be implemented in modified forms without departing from its essential characteristics.

[0210] Therefore, the disclosed embodiments should be considered illustrative rather than restrictive. The scope of the present invention is determined by the claims, not the foregoing description, and all differences within the scope equivalent thereto should be construed as being encompassed by the present invention.

[0211] Meanwhile, the methods according to the various embodiments of the present invention described above can be implemented as programs and provided to servers or devices. Accordingly, each device can access the server or device where the program is stored and download the program.

[0212] In addition, the methods according to the various embodiments of the present invention described above may be implemented as programs and stored and provided on various non-transitory computer-readable media. A non-transitory computer-readable medium refers to a medium that permanently stores data and can be read by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specifically, the various applications or programs described above may be stored and provided on non-transitory computer-readable media, such as a CD, DVD, hard disk, Blu-ray disk, USB, memory card, or ROM.

[0213] In addition, although the preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above, and various modifications can be made by a person having ordinary skill in the art to which the invention pertains without departing from the gist of the present invention claimed in the claims, and such modifications should not be understood individually from the technical idea or prospect of the present invention.

Claims

1. In a preprocessing method of height measurement data for growth prediction performed by a computing device, A step of inputting the subject's time-series biological component data; A step of entering identification data of the subject; A step of extracting height data from the above biocomponent data; A step of determining whether there is a decreasing section within the section in which the above-mentioned height data is input; and A preprocessing method of height measurement data for growth prediction, comprising: a step of determining an error in the height data based on a growth stage in which the height data includes a decreasing section, if a decreasing section of the height data exists; 2. In paragraph 1, A preprocessing method of height measurement data for growth prediction, characterized in that it further comprises a step of generating first data by connecting the time-series biocomponent data of the subject and the identification data of the subject.

3. In paragraph 2, A preprocessing method of biocomponent measurement data for growth prediction, characterized in that it further comprises a step of deleting the biocomponent data if the biocomponent data contains a preset value after generating the first data.

4. In paragraph 1, A preprocessing method of height measurement data for growth prediction, characterized in that the growth stages include a normal growth phase, a rapid growth phase, a decelerated growth phase, and a growth plate phase, and are classified based on the monthly age of the identification data.

5. In paragraph 4, A preprocessing method of height measurement data for growth prediction, characterized in that, in the step of determining the above error, if the above reduction section is included in the above rapid growth period, the height data of the above reduction section is deleted.

6. In paragraph 4, The steps for judging the above error are: A preprocessing method for height measurement data for growth prediction, characterized in that it comprises the step of calculating the difference between the height data of any first point forming the reduction section and the height data of a second point at the corresponding monthly age of a plurality of sample subjects, when the reduction section is included in the general growth period.

7. In paragraph 6, The steps for judging the above error are: A preprocessing method of height measurement data for growth prediction, characterized in that it further comprises a step of comparing the difference value calculated at the first point with the difference value calculated at the second point.

8. In paragraph 7, A preprocessing method of height measurement data for growth prediction, characterized in that, in the step of determining the error, if the difference value calculated at the first point is smaller than the difference value calculated at the second point, the height data of the second point is deleted.

9. In paragraph 7, A preprocessing method of height measurement data for growth prediction, characterized in that, in the step of determining the error, if the difference value calculated at the first point is greater than or equal to the difference value calculated at the second point, the height data of the first point is deleted.

10. In paragraph 4, A preprocessing method of height measurement data for growth prediction, characterized in that, in the step of determining the above error, if the above reduction section is included in the deceleration growth period or the no-growth period, data of the reduction section is deleted based on the duration of the reduction section and the degree of reduction in height.

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

12. A computer-readable recording medium having recorded thereon a program for executing a method for preprocessing height measurement data for growth prediction as described in any one of claims 1 to 10.

13. In a preprocessing device for height measurement data for growth prediction, A first input unit for receiving time-series biological component data of a subject; A second input unit for receiving identification data of the subject; A kidney data extraction unit for extracting kidney data from the above biocomponent data; A reduction section determination unit that determines whether there is a section in which the height data decreases within the input section; If there is a decreasing section of the above-mentioned growth data, a growth stage classification unit that classifies the growth stage including the decreasing section; and A preprocessing device for height measurement data for growth prediction, comprising an error determination unit that determines an error in the height data based on a growth stage including the above-mentioned reduction section.

14. In paragraph 13, A preprocessing device for height measurement data for growth prediction, characterized in that it further includes a connecting unit that connects data input through the first input unit and the second input unit to generate first data.

15. In paragraph 14, A preprocessing device for height measurement data for growth prediction, characterized in that it further includes an error detection unit for deleting the biocomponent data when a preset value is measured in the biocomponent data among the first data.

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