KR20220112616A - System for providing diabetes risk prediction, method, and program for the same - Google Patents

Abstract

The present invention relates to a diabetes risk calculation system. The diabetes risk calculation system includes: an analysis server comprising at least one computer, calculating the severity of the metabolic syndrome based on an input metabolic syndrome occurrence factor value for an occurrence factor of the metabolic syndrome, and calculating the risk of diabetes based on a statistical technique from the calculated metabolic syndrome severity; and a client connected with the analysis server. The client receives the input metabolic syndrome occurrence factor value to provide the value to the analysis server, and receives and outputs the risk of diabetes from the analysis server. The occurrence factor of the metabolic syndrome includes fasting blood sugar, systolic blood pressure, waist circumference, high-density lipoprotein, and triglyceride. Therefore, the present invention is capable of achieving high accuracy and quickly preventing a risky situation.

Description

Diabetes risk calculation system, calculation method, and program

The present invention relates to a diabetes risk calculation system, calculation method, and program.

As we enter the age of super-aging at the fastest rate in the world, life expectancy is increasing, but healthy life expectancy is gradually decreasing.

Metabolic syndrome, which can be expressed as a collection of adult diseases, has steadily increased over the past 10 years.

Prevention and management of diabetes, one of the key chronic metabolic diseases of the metabolic syndrome, is a way to prolong a healthy lifespan. It is the best way to prevent the occurrence of Chronic Kidney Disease) and reduce the deaths caused by them.

However, based on the metabolic syndrome onset factors, only the severity of the metabolic syndrome is predicted, and the risk of developing diabetes based on the severity of the metabolic syndrome is not even predicted.

Korean Patent Publication No. 10-2124249, 2020.06.11.

By calculating the metabolic syndrome severity based on the onset factors of the metabolic syndrome and recognizing or calculating the risk of developing diabetes based on the calculated metabolic syndrome severity, the risk of diabetes that can be quickly and accurately prevented in advance An object of the present invention is to provide a calculation system, a calculation method, and a program.

An object of the present invention is to calculate the metabolic syndrome severity based on the input metabolic syndrome onset factor value for the metabolic syndrome onset factor, and to calculate the risk of diabetes on the basis of the statistical technique from the calculated metabolic syndrome severity. an analysis server for calculating , and a client connected to the analysis server, wherein the client receives the input metabolic syndrome onset factor value and provides it to the analysis server And, the metabolic syndrome onset factor can be achieved by a diabetes risk calculation system including fasting blood sugar, systolic blood pressure, waist circumference, high-density lipoprotein, and triglycerides.

An object of the present invention is to obtain an input metabolic syndrome onset factor value for a metabolic syndrome onset factor, a metabolic syndrome severity calculation step of calculating a metabolic syndrome severity based on the input metabolic syndrome onset factor value, and the metabolic syndrome severity based on a statistical technique in the method, comprising the step of calculating the risk of developing diabetes for calculating the risk of developing diabetes, wherein the metabolic syndrome onset factors include calculating the risk of developing diabetes including fasting blood sugar, systolic blood pressure, waist circumference, high-density lipoprotein, and triglycerides method can be achieved.

The object of the present invention can be achieved by a program for calculating the risk of developing diabetes, which is stored in a medium to execute the method for calculating the risk of developing diabetes in combination with a computer that is hardware.

According to the present invention, by calculating the metabolic syndrome severity based on the onset factors of the metabolic syndrome and recognizing or calculating the risk of developing diabetes based on the calculated metabolic syndrome severity, it is possible to prevent dangerous situations in advance with high accuracy and promptly. It is possible to provide a system for calculating the risk of developing diabetes, a method for calculating the risk, and a program.

1 is a block diagram schematically showing a system for calculating the risk of developing diabetes according to an embodiment of the present invention.
2 exemplarily shows the classification of the severity of metabolic syndrome into three stages.
FIG. 3 exemplarily shows the calculation of the risk of developing diabetes based on the multivariable Cox proportional hazards regression model in the severity of the metabolic syndrome of FIG. 2 .
4 is a flowchart schematically illustrating a method for calculating the risk of developing diabetes according to an embodiment of the present invention.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only the present embodiments are provided so that the disclosure of the present invention is complete and common in the technical field to which the present invention pertains. It is provided to fully inform those skilled in the art of the scope of the present invention, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural, unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, and "and/or" includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various elements, these elements are not limited by these terms, of course. These terms are only used to distinguish one component from another. Therefore, it goes without saying that the first component mentioned below may be the second component within the spirit of the present invention.

In the present specification, the computer includes various devices capable of visually presenting a result to a user by performing arithmetic processing. For example, computers include desktop PCs, notebooks (Note Books) as well as smart phones, tablet PCs, cellular phones, PCS phones (Personal Communication Service phones), synchronous/asynchronous A mobile terminal of International Mobile Telecommunication-2000 (IMT-2000), a Palm Personal Computer (PC), a Personal Digital Assistant (PDA), and the like may also be applicable. Also, the computer may be a medical device for acquiring or observing a medical image. Also, the computer may correspond to a server computer connected to various client computers.

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless clearly specifically defined.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram schematically showing a system for calculating the risk of developing diabetes according to an embodiment of the present invention.

Referring to FIG. 1 , the diabetes risk calculation system 10 according to an embodiment of the present invention includes an analysis server 100 and a client 200 .

The analysis server 100 is composed of one or more computers. The analysis server 100 calculates the metabolic syndrome severity based on the input metabolic syndrome onset factor value according to the metabolic syndrome onset factor. The analysis server 100 calculates the risk of developing diabetes using statistical techniques in the severity of metabolic syndrome.

In the present specification, "metabolic syndrome" in the pathogenesis of metabolic syndrome may include, for example, at least one of hypertension, dyslipidemia, and diabetes.

Factors in the development of metabolic syndrome include, for example, fasting blood sugar, systolic blood pressure, waist circumference, high-density lipoprotein, and triglycerides.

The analysis server 100, for example, statistically analyzes the Korea Genomic Epidemiology Research cohort data and calculates the metabolic syndrome severity through the metabolic syndrome severity formula (Equation 1 or 2 below) based on the health checkup data. This is used to calculate the risk of diabetes invention. For example, when the input metabolic syndrome onset factor value is provided from a man over 40 years old, the analysis server 100 may calculate the metabolic syndrome severity using Equation 1 below.

In Equation 1, FPG is fasting blood sugar (mg/dL), SBP is systolic blood pressure (mmHg), WC is waist circumference (cm), HDL is high-density lipoprotein (mg/dL), TG is triglyceride (mg/dL) )to be.

The analysis server 100 may calculate the metabolic syndrome severity by Equation 2 below, for example, when the input metabolic syndrome onset factor value is provided from a woman over 40 years old.

In Equation 2, FPG is fasting blood glucose (mg/dL), SBP is systolic blood pressure (mmHg), WC is waist circumference (cm), HDL is high-density lipoprotein (mg/dL), TG is triglyceride (mg/dL) )to be.

The analysis server 100 may classify the severity of metabolic syndrome into a specific range, for example, and calculate the risk of diabetes by a statistical technique. The analysis server 100 may, for example, divide the severity of metabolic syndrome into three ranges, and calculate the risk of developing diabetes by a statistical technique. For example, Figure 2 shows that the metabolic syndrome onset factor values are provided for men over 40, and women over 40, calculate the metabolic syndrome severity, and divide the range (quantiles) into three stages. . In FIG. 2 , R (ansung) denotes a country, and U (ansan) denotes a city.

For example, the metabolic syndrome severity for men in their 40s was less than -0.4869; Metabolic syndrome severity value in the 2nd quartile group: -0.4868 or more and less than +0.1875, and the metabolic syndrome severity value in the 3rd quartile group: 0.1875 or more. For example, for women in their 40s, the severity of metabolic syndrome was less than 0.0140; The metabolic syndrome severity value of the second quartile group: 0.0140 or more and less than 0.7100, and the metabolic syndrome severity value of the third quartile group: 0.7100 or more.

The statistical technique may be, for example, a Multivariable Cox proportional hazards regression model. The Cox proportional hazards regression model is a model that calculates the relative hazard for specific variables. Through the model, the effects of a plurality of prognostic variables related to the survival probability (or the occurrence probability of a certain disease) of a certain group, for example, the occurrence probability of T2DM, can be simultaneously viewed while controlling for a plurality of confounding variables. Referring to FIG. 3 , it can be seen that the calculation of the risk of diabetes on the basis of the multivariable Cox proportional hazards regression model from the severity of the metabolic syndrome of FIG. 2 is exemplarily shown.

3 shows the analysis according to model A, model B, and model C, in which the influence of the confounding variable is corrected. Model A is an analysis that corrects for only two confounding variables of region and age, model B is an analysis that additionally corrects for the effects of confounding variables of drinking, smoking, exercise, and family history in addition to model A, and the model C is an analysis in which model B is further corrected for a confounding variable called BMI. The system for calculating the risk of developing diabetes according to an embodiment of the present invention may be based on model C, in which confounding variables are maximally corrected.

For example, referring to FIGS. 2 and 3 , compared with the reference group, the first quartile group, the probability of developing diabetes is 1.53 times for the male quartile group, 3.01 times for the third quartile group, and the second quartile group for women. It can be interpreted that the probability of this occurrence is 2.00 times, and in the case of the third quartile group, 3.98 times.

For example, if a 45-year-old female individual with a metabolic syndrome severity value of 0.57 is calculated, it can be interpreted that the risk of developing diabetes is about twice that of those in the metabolic syndrome severity quintile group with a metabolic syndrome severity value of less than 0.0140. .

Referring back to FIG. 1 , the risk of developing diabetes may include, for example, at least one of an onset time of diabetes and an incidence of diabetes.

The diabetes onset risk calculation system 10 according to an embodiment of the present invention may calculate the diabetes onset risk by using one or more computers, using a deep learning learning model.

Hereinafter, a deep learning learning model will be described.

The analysis server 100 is composed of one or more computers to form a deep learning learning model, and serves to determine the degree of risk of developing diabetes based on the input metabolic syndrome onset factor value or the calculated metabolic syndrome severity.

In one embodiment, the analysis server 100 may form a plurality of deep learning learning models. For example, based on the input metabolic syndrome onset factor value, the first deep learning learning model for calculating the metabolic syndrome severity, and or the second deep learning for determining the risk of developing diabetes based on the calculated metabolic syndrome severity It may include a learning model.

A deep learning learning model according to embodiments of the present invention refers to a system or network that builds one or more layers in one or more computers and performs judgment based on a plurality of data. For example, a deep learning learning model may be implemented with a set of layers including a convolutional pooling layer, a locally-connected layer, and a fully-connected layer. . A convolutional pooling layer or a local access layer may be configured to extract features in an image. The fully connected layer can determine the correlation between features of the image. In some embodiments, the overall structure of the deep learning learning model may be in the form of a convolutional pooling layer followed by a local access layer, and a fully connected layer falling on the local access layer. The deep learning learning model may include various judgment criteria (ie, parameters), and may add new judgment criteria (ie, parameters) through input image analysis. Parameters may include, for example, fasting blood sugar, systolic blood pressure, waist circumference, high-density lipoprotein, and triglycerides. The parameter further includes, for example, at least one of a high-density lipoprotein (HDL) cholesterol level, alcohol consumption, smoking status, exercise status, family history, past medical history, current medical history, BMI, age, height, weight, and living environment. can do.

A deep learning learning model according to embodiments of the present invention is a structure called a convolutional neural network suitable for image analysis, and a feature extraction layer that learns by itself a feature with the greatest discriminative power from given image data. Layer) and a prediction layer that learns a calculation model to produce the highest calculation performance based on the extracted features may be configured in an integrated structure.

The feature extraction layer spatially integrates the convolution layer, which creates a feature map by applying a plurality of filters to each region of the image, and the feature map to obtain features that are invariant to changes in position or rotation. It may be formed in a structure in which a pooling layer that can be extracted is alternately repeated several times. Through this, various level features can be extracted from low-level features such as points, lines, and planes to complex and meaningful high-level features.

The convolutional layer obtains a feature map by taking a nonlinear activation function on the dot product of the filter and the local receptive field for each patch of the input image. Therefore, CNN is characterized by using filters with sparse connectivity and shared weights. This connection structure reduces the number of parameters to be learned, makes learning through the backpropagation algorithm efficient, and consequently improves the output performance.

The integration layer (Pooling Layer or Sub-sampling Layer) generates a new feature map by using local information of the feature map obtained from the previous convolutional layer. In general, the newly created feature map by the integration layer is reduced to a smaller size than the original feature map. Representative integration methods include Max Pooling, which selects the maximum value of the corresponding region in the feature map, and the corresponding feature map in the feature map. There is an average pooling method that calculates the average value of a region. In general, the feature map of the integrated layer may be less affected by the position of an arbitrary structure or pattern existing in the input image than the feature map of the previous layer. That is, the integration layer can extract features that are more robust to regional changes such as noise or distortion in the input image or previous feature map, and these features can play an important role in classification performance. Another role of the integration layer is to reflect the characteristics of a wider area as you go up to the upper learning layer in the deep structure. It is possible to generate features that reflect the characteristics of the abstract image as a whole.

As such, the features finally extracted through repetition of the convolutional layer and the integration layer are fully connected to the classification model such as multi-layer perception (MLP) or support vector machine (SVM). -connected layer) and can be used for classification model training and calculation.

However, the structure of the deep learning learning model according to the embodiments of the present invention is not limited thereto, and may be formed of a neural network of various structures.

The client 200 receives the input metabolic syndrome onset factor value and provides it to the analysis server 100 . The client 200 receives and outputs the diabetes risk from the analysis server 100 .

The client 200 may include an input device 210 and an output device 220 . The input device 210 and the output device 220 may be implemented as one device or may be implemented as separate devices.

The input device 210 may be a device that receives the metabolic syndrome onset factor and transmits the corresponding input metabolic syndrome onset factor value to the analysis server 100 .

In an embodiment, the input device 210 may receive the blood of a user who needs to analyze the risk of developing diabetes, and may calculate metabolic syndrome onset factors.

There may be one input device 210 or a plurality of input devices 210 .

The output device 220 may receive the diabetes onset risk result from the analysis server 100 and provide the determination result to the user in various ways. For example, the output device 220 may include a display unit to visually display the risk of diabetes and provide it to the user.

Also, when receiving the determination result that the risk of developing diabetes is high, the output device 220 may generate vibration to notify the user that the risk of developing diabetes is high.

However, the method in which the output device 220 provides the result of determining the risk of diabetes to the user is not limited thereto, and various output methods that can be provided to the user, such as sound output, may be used. Also, the output device 220 according to an embodiment of the present invention may include a mobile terminal.

There may be one output device 220 or a plurality of output devices 220 .

Hereinafter, a method for calculating the risk of developing diabetes according to an embodiment of the present invention will be described. Hereinafter, differences from the aforementioned system for calculating the risk of developing diabetes according to an embodiment of the present invention will be described in detail, and parts not described above may be the same as the system for calculating the risk of developing diabetes according to an embodiment of the present invention. have.

4 is a flowchart schematically illustrating a method for calculating the risk of developing diabetes according to an embodiment of the present invention.

1 and 4, the method for calculating the risk of developing diabetes according to an embodiment of the present invention includes the steps of obtaining an input metabolic syndrome onset factor value for the metabolic syndrome onset factor (S100), a metabolic syndrome severity calculation step ( S200), and a diabetes risk calculation step (S300).

An input metabolic syndrome onset factor value for the metabolic syndrome onset factor is acquired (S100). The input metabolic syndrome onset factor value may be input from the client 200 and provided to the analysis server 100 . The input metabolic syndrome onset factor value may be input to the input device 210 and provided to the analysis server 100 .

The metabolic syndrome severity is calculated based on the input metabolic syndrome onset factor value (S200). The metabolic syndrome severity calculation step ( S200 ) may be performed in the analysis server 100 .

In one embodiment, the metabolic syndrome severity calculation step ( S200 ) may calculate the metabolic syndrome severity through a metabolic syndrome severity formula derived based on Ansung-Ansan cohort data.

In an embodiment, the metabolic syndrome severity calculation step S200 may calculate the metabolic syndrome severity using Equation 1 described above when the input metabolic syndrome onset factor value is provided from a man over 40 years of age.

In an embodiment, the metabolic syndrome severity calculation step S200 may calculate the metabolic syndrome severity using Equation 2 described above when the input metabolic syndrome onset factor value is provided from a woman over 40 years old.

A statistical technique from the severity of the metabolic syndrome calculates the risk of developing diabetes (S300). The risk of developing diabetes may be provided from the analysis device 100 to the output device 220 . The output device 220 may be implemented as a single device with the input device 210 , or may be implemented as separate devices.

As an embodiment, in the step of calculating the risk of developing diabetes ( S300 ), the severity of metabolic syndrome may be divided into a specific range, and the risk of developing diabetes may be calculated using a statistical technique.

As an embodiment, in the step of calculating the risk of developing diabetes ( S300 ), the severity of metabolic syndrome may be divided into three ranges, and the risk of developing diabetes may be calculated using a statistical technique. For example, in Figure 2, men in their 40s or older, and women in their 40s or older receive metabolic syndrome onset factor values, and apply Equation 1 and Equation 2 to individuals included in the Cocht data to metabolize It indicates that the syndrome severity was calculated and the range was divided into three stages.

For example, the metabolic syndrome severity for men in their 40s was less than -0.4869; Metabolic syndrome severity value in the second quartile group: -0.4868 or more and less than +0.1875, and the metabolic syndrome severity value in the third quartile group: 0.1875 or more. For example, for women in their 40s, the severity of metabolic syndrome was less than 0.0140; The metabolic syndrome severity value of the second quartile group: 0.0140 or more and less than 0.7100, and the metabolic syndrome severity value of the third quartile group: 0.7100 or more.

In one embodiment, the statistical technique used in the step of calculating the risk of developing diabetes ( S300 ) may be a multivariable Cox proportional hazards regression model. The Cox proportional hazards regression model is a model that calculates the relative hazard for specific variables. Through the model, the effects of a plurality of prognostic variables related to the survival probability (or the occurrence probability of a certain disease) of a certain group, for example, the occurrence probability of T2DM, can be simultaneously viewed while controlling for a plurality of confounding variables.

Referring to FIG. 3 , it can be seen that the calculation of the risk of diabetes on the basis of the multivariable Cox proportional hazards regression model from the severity of the metabolic syndrome of FIG. 2 is exemplarily shown.

For example, referring to FIGS. 2 and 3 , compared with the reference group, the first quartile group, the probability of developing diabetes is 1.53 times for the male quartile group, 3.01 times for the third quartile group, and the second quartile group for women. It can be interpreted that the probability of this occurrence is 2.00 times, and in the case of the third quartile group, 3.98 times.

For example, if a 45-year-old female individual with a metabolic syndrome severity value of 0.57 is calculated, it can be interpreted that the risk of developing diabetes is about twice that of those in the metabolic syndrome severity quintile group with a metabolic syndrome severity value of less than 0.0140. .

In one embodiment, the step of calculating the risk of developing diabetes ( S300 ) is by using one or more computers, using a deep learning learning model, learning the input metabolic syndrome onset factor value, the metabolic syndrome severity, and at least one and the risk of developing diabetes as a dataset It may include the step of calculating the risk of developing diabetes with a learning step, and a deep learning learning model.

In one embodiment, the step of calculating the risk of developing diabetes (S300) is a learning step of learning the metabolic syndrome severity and the risk of developing diabetes from a dataset by one or more computers, using a deep learning learning model, and a deep learning learning model. It may include calculating the risk of developing diabetes.

The risk of developing diabetes may be determined, for example, by parameters of the aforementioned deep learning learning model.

The method for calculating the risk of developing diabetes according to an embodiment of the present invention may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium included in the system for calculating the risk of developing diabetes.

A program is a code coded in a computer language such as C, C++, JAVA, or machine language that the computer's processor (CPU) can read through the computer's device interface in order for the computer to read the program and execute the methods implemented as the program. (Code) may be included. Such code may include functional code related to a function defining functions necessary for executing the methods, etc. . In addition, the code may further include additional information necessary for the processor of the computer to execute functions or memory reference related code for which location (address address) in the internal or external memory of the computer should be referenced. In addition, if the computer's processor needs to communicate with any other computer or server located remotely in order to execute functions, the code determines how to communicate with any other computer or server remotely using the computer's communication module. , may further include a communication-related code for which information or media should be transmitted and received during communication.

The storage medium is not a medium that stores data for a short moment, such as a register, a cache, a memory, etc., but a medium that stores data semi-permanently and can be read by a device. Specifically, examples of the storage medium include, but are not limited to, ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like. That is, the program may be stored in various recording media on various servers that the computer can access or in various recording media on the user's computer. In addition, the medium may be distributed in a computer system connected through a network, and computer readable codes may be stored in a distributed manner.

Diabetes incidence risk calculation system, calculation method, and program according to an embodiment of the present invention calculates the metabolic syndrome severity based on the onset factors of the metabolic syndrome, and recognizes or calculates the diabetes incidence risk based on the calculated metabolic syndrome severity Accordingly, it is possible to prevent dangerous situations in advance with high accuracy and promptly.

In the above, the embodiments of the present invention have been described with reference to the accompanying drawings, but those skilled in the art to which the present invention pertains can practice the present invention in other specific forms without changing its technical spirit or essential features. You will understand that there is Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

10: Diabetes risk calculation system
100: analysis server
200: client
210: input device
220: output device

Claims (19)

An analysis server that is composed of one or more computers, calculates the metabolic syndrome severity based on the input metabolic syndrome onset factor value for the metabolic syndrome onset factor, and calculates the risk of diabetes on the basis of a statistical technique from the calculated metabolic syndrome severity ; and
Including; a client connected to the analysis server;
the client is
receiving the input metabolic syndrome onset factor value and providing it to the analysis server,
Receive and output the risk of developing diabetes from the analysis server,
The causes of the metabolic syndrome are
Diabetes risk calculation system including fasting blood sugar, systolic blood pressure, waist circumference, high-density lipoprotein, and triglycerides. According to claim 1,
The analysis server
A diabetes risk calculation system that statistically analyzes cohort data from the Korea Genome Epidemiology Research and calculates the risk of developing diabetes through the metabolic syndrome severity formula. 3. The method of claim 2,
The analysis server
When the input metabolic syndrome onset factor value is provided from a man over 40 years old, the diabetes onset risk calculation system for calculating the diabetes onset risk based on Equation 1 below:
[Equation 1]
-8.2939 + 0.0126 × FPG + 0.0063 × SBP + 0.0382 × WC - 0.0210 × HDL + 0.8432 × ln (TG),
In Equation 1, FPG is fasting blood sugar (mg/dL), SBP is systolic blood pressure (mmHg), WC is waist circumference (cm), HDL is high-density lipoprotein (mg/dL), TG is triglyceride (mg/dL) )to be. 3. The method of claim 2,
The analysis server
When the input metabolic syndrome onset factor value is provided from a woman over 40 years old, a diabetes onset risk calculation system for calculating the diabetes onset risk based on Equation 2 below:
[Equation 2]
-7.5210 + 0.0156 × FPG + 0.0073 × SBP + 0.0292 × WC - 0.0207 × HDL + 0.9065 × ln (TG),
In Equation 2, FPG is fasting blood glucose (mg/dL), SBP is systolic blood pressure (mmHg), WC is waist circumference (cm), HDL is high-density lipoprotein (mg/dL), TG is triglyceride (mg/dL) )to be. According to claim 1,
The analysis server
A system for calculating the risk of developing diabetes by dividing the severity of the metabolic syndrome into a specific range and calculating the risk of developing diabetes by the statistical technique. According to claim 1,
The statistical technique is
A multivariable Cox proportional hazards regression model, diabetes risk calculation system. According to claim 1,
The analysis server
Using a deep learning learning model, learning the metabolic syndrome severity and the risk of developing diabetes as a dataset,
A diabetes risk calculation system for calculating the risk of developing diabetes with the deep learning learning model. According to claim 1,
The risk of developing diabetes is
Diabetes incidence risk calculation system including at least one of the onset time of the diabetes and the incidence rate of the diabetes. According to claim 1,
the client is
an input device receiving the input metabolic syndrome onset factor value and providing it to the analysis server; and
and an output device for receiving and outputting the diabetes risk from the analysis server. obtaining an input metabolic syndrome onset factor value for the metabolic syndrome onset factor;
a metabolic syndrome severity calculation step of calculating a metabolic syndrome severity based on the input metabolic syndrome onset factor value; and
A diabetes risk calculation step of calculating the risk of developing diabetes based on a statistical technique in the severity of the metabolic syndrome;
The causes of the metabolic syndrome are
A method of calculating the risk of developing diabetes, including fasting blood sugar, systolic blood pressure, waist circumference, high-density lipoprotein, and triglycerides. 11. The method of claim 10,
The risk of developing diabetes is
A method of calculating the risk of developing diabetes, which is calculated through the metabolic syndrome severity equation by statistically analyzing cohort data from the Korea Genome Epidemiology Study. 12. The method of claim 11,
The risk of developing diabetes is
When the input metabolic syndrome onset factor value is provided from a man over 40 years old, a method of calculating the risk of diabetes on the basis of Equation 1 below:
[Equation 1]
-8.2939 + 0.0126 × FPG + 0.0063 × SBP + 0.0382 × WC - 0.0210 × HDL + 0.8432 × ln (TG),
In Equation 1, FPG is fasting blood sugar (mg/dL), SBP is systolic blood pressure (mmHg), WC is waist circumference (cm), HDL is high-density lipoprotein (mg/dL), TG is triglyceride (mg/dL) )to be. 12. The method of claim 11,
The risk of developing diabetes is
When the input metabolic syndrome onset factor value is provided from a woman over 40 years old, a method of calculating the risk of diabetes on the basis of Equation 2 below:
[Equation 2]
-7.5210 + 0.0156 × FPG + 0.0073 × SBP + 0.0292 × WC - 0.0207 × HDL + 0.9065 × ln (TG),
In Equation 2, FPG is fasting blood glucose (mg/dL), SBP is systolic blood pressure (mmHg), WC is waist circumference (cm), HDL is high-density lipoprotein (mg/dL), TG is triglyceride (mg/dL) )to be. 11. The method of claim 10,
The step of calculating the risk of developing diabetes is
A method of calculating the risk of developing diabetes by dividing the severity of the metabolic syndrome into a specific range and calculating the risk of developing diabetes by the statistical technique. 11. The method of claim 10,
The statistical technique is
A method of calculating the risk of developing diabetes, a multivariable Cox proportional hazards regression model. 11. The method of claim 10,
The step of calculating the risk of developing diabetes is
Using a deep learning learning model, learning the metabolic syndrome severity and the risk of developing diabetes as a dataset,
A method of calculating the risk of developing diabetes by using the deep learning learning model to calculate the risk of developing diabetes. 11. The method of claim 10,
The risk of developing diabetes is
A method for calculating a risk of developing diabetes including at least one of the onset time of the diabetes and the incidence of diabetes. 11. The method of claim 10,
The method of calculating the risk of developing diabetes further comprising the step of receiving and outputting the calculated risk of developing diabetes. In combination with a computer, which is hardware, stored in the medium to execute the method for calculating the risk of developing diabetes according to claims 10 to 18, the program for calculating the risk of developing diabetes.

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