KR102599132B1 - System for providing disease risk prediction based on bigdata, method, and program for the same - Google Patents

Abstract

The present invention relates to a big data-based disease onset risk prediction system, which consists of one or more computers, collects input metabolic syndrome onset factor values for metabolic syndrome onset factors over time to form big data, and forms big data. An analysis server that analyzes data and predicts the risk of developing a specific disease; and a client connected to the analysis server, wherein the client receives the input metabolic syndrome onset factor value, provides the value to the analysis server, receives the risk of developing the disease from the analysis server, and outputs the metabolic syndrome onset factor value. The onset factors include abdominal circumference, blood pressure, blood sugar, HDL (high-density lipoprotein) cholesterol, and triglycerides, and the analysis server determines that at least three of the input metabolic syndrome onset factor values exceed the reference value set based on the big data. In this case, the risk of developing the specific disease is predicted.

Description

Big data-based disease onset risk prediction system, prediction method, and program {SYSTEM FOR PROVIDING DISEASE RISK PREDICTION BASED ON BIGDATA, METHOD, AND PROGRAM FOR THE SAME}

The present invention relates to a big data-based disease onset risk prediction system, prediction method, and program.

As we enter the era of super-aging at the fastest rate around the world, life expectancy is increasing, but healthy life expectancy is decreasing little by little. For a “healthy age of centenarians,” the biggest key is to narrow the gap between the two.

Metabolic syndrome, which can be expressed as a collection of adult diseases, has been steadily increasing over the past 10 years, and the prevalence of metabolic syndrome has recently increased from 1 in 4 adults in 1998 to 1 in 3.

A way to increase healthy lifespan is to prevent and manage the three core chronic metabolic diseases of metabolic syndrome: hypertension, dyslipidemia, and diabetes, as well as coronary artery disease (CAD), stroke (Cerebral Vascular Accident), and It is the best way to prevent the occurrence of chronic kidney disease (CKD) and reduce deaths from it.

However, the occurrence status of metabolic syndrome is very different depending on gender and age, and appropriate management indicators are needed to prevent and manage Korean metabolic syndrome, which is different from that of Westerners.

The highest level of evidence for establishing guidelines for the prevention and management of chronic metabolic diseases is the randomized controlled trial (RCT). However, randomized controlled clinical studies have the problem of requiring a large number of study subjects and a long follow-up period.

Korean Patent Publication No. 10-2124249, 2020.06.11.

Big data-based disease development risk that can prevent dangerous situations in advance by generating and analyzing big data based on the onset factors of metabolic syndrome accumulated over time and recognizing or predicting the occurrence and risk of specific diseases. It is intended to provide prediction systems, prediction methods, and programs.

The purpose of the present invention is to collect the input metabolic syndrome onset factor values for metabolic syndrome onset factors over time, which consists of one or more computers, to form big data, and to analyze the big data to determine the risk of developing a specific disease. includes a prediction analysis server, and a client connected to the analysis server; wherein the client receives the input metabolic syndrome onset factor value, provides the value to the analysis server, and receives the risk of developing the disease from the analysis server. Output, the metabolic syndrome onset factors include abdominal circumference, blood pressure, blood sugar, HDL cholesterol, and triglycerides, and the analysis server determines that at least three of the input metabolic syndrome onset factor values exceed the reference value set based on the big data. In this case, it can be achieved by a big data-based disease onset risk prediction system that predicts the disease onset risk of the specific disease.

The purpose of the present invention is to input an input metabolic syndrome onset factor value for the onset factor of metabolic syndrome, a big data collection step of collecting the input metabolic syndrome onset factor value over time to form big data, and the big data. A disease onset risk prediction step of predicting the risk of developing a specific disease by analyzing the disease onset risk, wherein the metabolic syndrome onset factors include abdominal circumference, blood pressure, blood sugar, HDL cholesterol, and triglycerides, and predicting the risk of developing the disease. The step is achieved by a big data-based disease onset risk prediction method that predicts the disease onset risk of the specific disease when at least three of the input metabolic syndrome onset factor values exceed the reference value set based on the big data. You can.

The purpose of the present invention can be achieved by a big data-based disease onset risk prediction system that is combined with hardware, a computer, and stored in a medium to execute the big data-based disease onset risk prediction method.

According to the present invention, big data is generated and analyzed based on metabolic syndrome onset factors accumulated over time to recognize or predict the occurrence and risk of a specific disease, thereby preventing dangerous situations in advance. A disease onset risk prediction system, prediction method, and program can be provided.

According to the present invention, it is possible to provide a big data-based disease development risk prediction system, prediction method, and program that is comparable to the results of randomized controlled clinical studies and can manage metabolic syndrome in Koreans.

Figure 1 is a block diagram schematically showing a big data-based disease onset risk prediction system according to an embodiment of the present invention.
Figure 2 is a flowchart schematically showing a big data-based disease onset risk prediction method according to an embodiment of the present invention.

The advantages and features of the present invention and methods for achieving them will become clear by referring to the embodiments described in detail below along with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms. The present embodiments are merely intended to make the disclosure of the present invention complete and to provide a general understanding of 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 describing embodiments and is not intended to limit the invention. As used herein, singular forms also include plural forms, unless specifically stated otherwise in the context. As used in the specification, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other elements in addition to the mentioned elements. Like reference numerals refer to like elements throughout the specification, and “and/or” includes each and every combination of one or more of the referenced elements. Although “first”, “second”, etc. are used to describe various components, these components are of course not limited by these terms. These terms are merely used to distinguish one component from another. Therefore, it goes without saying that the first component mentioned below may also be a second component within the technical spirit of the present invention.

In this specification, a computer includes all various devices that can perform computational processing and visually present the results to the user. For example, computers include not only desktop PCs and laptops (Note Books), but also smart phones, tablet PCs, cellular phones, PCS phones (Personal Communication Service phones), and synchronous/asynchronous computers. This may also include IMT-2000 (International Mobile Telecommunication-2000) mobile terminals, Palm Personal Computers (Palm PCs), and personal digital assistants (PDAs). Additionally, computers may also refer to medical equipment that acquires or observes medical images. Additionally, the computer may be a server computer connected to various client computers.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which the present invention pertains. Additionally, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless clearly specifically defined.

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

Figure 1 is a block diagram schematically showing a big data-based disease onset risk prediction system according to an embodiment of the present invention.

Referring to FIG. 1, the big data-based disease onset risk prediction system 10 according to an embodiment of the present invention includes an analysis server 100 and a client 200.

The analysis server 100 consists of one or more computers. The analysis server 100 collects input metabolic syndrome onset factor values over time according to the metabolic syndrome onset factors, and forms big data 110. The analysis server 100 analyzes the big data 110 and predicts the risk of developing a specific disease.

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

In this specification, “collection over time” may mean, for example, that the input metabolic syndrome onset factor value is provided at least once before the current input metabolic syndrome onset factor value is provided. For example, collection over time may mean collecting input metabolic syndrome predisposition factor values provided over the past three years.

In one embodiment, the big data 110 may be National Health Insurance Corporation big data.

Factors contributing to metabolic syndrome include, for example, abdominal circumference, blood pressure, blood sugar, HDL cholesterol, and triglycerides.

The analysis server 100 may accumulate and collect the values of input metabolic syndrome onset factors over a specific period of time. For example, the analysis server 100 may accumulate and collect the values of input metabolic syndrome onset factors over a period of 5 years. For example, the analysis server 100 may accumulate and collect values of input metabolic syndrome onset factors over the past three years. For example, the analysis server 100 may accumulate and collect values of input metabolic syndrome onset factors over the past five years.

The analysis server 100 analyzes big data 110. The analysis server 100 may analyze the big data 110 using, for example, logistic regression analysis.

The analysis server 100 can predict the risk of developing the disease if at least three of the input metabolic syndrome onset factor values exceed the reference value set based on big data.

For example, the reference value may be a standard that can evaluate the absolute value of the input metabolic syndrome onset factor value, or it may be a standard that can evaluate the difference between the input metabolic syndrome onset factor value and a normal value set based on big data.

In one embodiment, the analysis server 100 determines whether the input metabolic syndrome onset factor values, for example, at least three of abdominal circumference, blood pressure, blood sugar, HDL cholesterol, and neutral fat, exceed the reference value set based on big data. By determining whether it is exceeded or not exceeded, the risk of developing a disease can be predicted.

Specific diseases whose risk is predicted from the disease onset risk may include, for example, at least one of hypertension, dyslipidemia, diabetes, coronary artery disease, stroke, and chronic kidney disease.

In one embodiment, the analysis server 100 may predict the risk of metabolic syndrome including at least one of high blood pressure, dyslipidemia, and diabetes based on the big data 110. For example, if at least three of the input metabolic syndrome onset factor values exceed the reference value set based on the big data 110, the analysis server 100 determines the metabolic syndrome including at least one of hypertension, dyslipidemia, and diabetes. The risk can be predicted.

In one embodiment, the analysis server 100 can predict the risk of chronic metabolic disease, including at least one of coronary artery disease, stroke, and chronic kidney disease, based on the big data 110 and the risk of metabolic syndrome. . For example, if at least one of the predicted risks of hypertension, dyslipidemia, and diabetes exceeds the reference value set based on big data (110), the risk of developing chronic metabolic disease, in which metabolic syndrome has developed, can be predicted. .

In one embodiment, if the risk of high blood pressure and diabetes is predicted, the risk of stroke can be predicted.

In one embodiment, if the risk of hypertension, dyslipidemia, and diabetes is predicted, the risk of coronary artery disease can be predicted.

In one embodiment, if the risk of dyslipidemia and diabetes is predicted, the risk of chronic kidney disease can be predicted.

In one embodiment, if the risk of dyslipidemia is predicted, the risk of coronary artery disease can be predicted.

The risk of developing a disease may include, for example, at least one of the onset time of a specific disease and the incidence of a specific disease.

The big data-based disease onset risk prediction system 10 according to an embodiment of the present invention can predict the disease onset risk using a deep learning learning model 120 by one or more computers.

Below, the deep learning learning model 120 will be described.

The analysis server 100 is composed of one or more computers to form a deep learning model 120, and serves to determine the degree of risk of developing the disease based on the input metabolic syndrome onset factor value.

The deep learning learning model 120 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 makes decisions based on a plurality of data. For example, the deep learning learning model 120 is implemented as a set of layers including a convolutional pooling layer, a locally-connected layer, and a fully-connected layer. It can be. A convolutional pooling layer or local connection layer can be configured to extract features within the image. The fully connected layer can determine correlations between features of the image. In some embodiments, the overall structure of the deep learning model 120 may be a convolutional pooling layer followed by a local connection layer, and a local connection layer followed by a fully connected layer. The deep learning learning model 120 may include various judgment standards (i.e., parameters), and new judgment standards (i.e., parameters) may be added through analysis of input images. Parameters may include, for example, abdominal circumference, blood pressure, blood sugar, high-density lipoprotein (HDL) cholesterol, and triglycerides. The parameters may further include at least one of drinking status, smoking status, exercise status, family history, past medical history, current medical history, BMI, age, height, weight, and living environment.

In one embodiment, if the parameters include abdominal circumference and gender, if you are a man, for example, if your abdominal circumference is greater than 90 cm, if you are a woman, for example, if your abdominal circumference is greater than 85 cm, you may be at risk for metabolic syndrome; If it is less than , it may be normal.

In one embodiment, if the parameter includes blood pressure, if the systolic blood pressure (SBP) value is, for example, 130 mmHg or higher or the diastolic blood pressure (DBP: Diastolic blood pressure) value is, for example, 80 mmHg or higher, there is a risk of metabolic syndrome. It may be normal, and if it is outside the above range, it may be normal.

In one embodiment, if the parameter includes blood sugar, for example, if the blood sugar value is above 100 mg/dL, there may be a risk of metabolic syndrome, and if it is below 100 mg/dL, it may be normal.

In one embodiment, if the parameter includes HDL cholesterol, for example, if the HDL cholesterol value is above 240 mg/dL, it may be at risk for metabolic syndrome, and if it is below, it may be normal.

In one embodiment, if the parameter includes triglycerides, for example, if the triglyceride value is more than 150 mg/dL, it may be at risk for metabolic syndrome, and if it is less than 150 mg/dL, it may be normal.

The deep learning learning model 120 according to embodiments of the present invention is a structure called a convolutional neural network suitable for image analysis, and is a feature extraction layer that automatically learns the feature with the greatest discriminative power from the given image data. It can be composed of an integrated structure of the Feature Extraction Layer and the Prediction Layer, which learns a prediction model to produce the highest prediction performance based on the extracted features.

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

The convolution layer obtains a feature map by taking a non-linear activation function as the inner product of the filter and the local receptive field for each patch of the input image, and compares it with other network structures. Therefore, CNN is characterized by using filters with sparse connectivity and shared weights. This connection structure reduces the number of parameters to learn, makes learning through the backpropagation algorithm efficient, and ultimately improves prediction performance.

The integration layer (Pooling Layer or Sub-sampling Layer) creates a new feature map using the local information of the feature map obtained from the previous convolution layer. In general, the feature map newly created 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 area within the feature map, and There is average pooling, which calculates the average value of an area. The feature map of the integrated layer is generally less affected by the location of arbitrary structures or patterns present in the input image than the feature map of the previous layer. In other words, the integration layer can extract features that are more robust to local 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 higher learning layer in the deep structure. As the feature extraction layer accumulates, local characteristics are reflected in the lower layers, and as you go up to the upper layer, you can reflect the characteristics of a wider area. Features that reflect the characteristics of the entire abstract image can be generated.

In this way, the features finally extracted through repetition of the convolutional layer and the integration layer are classified into fully connected layers such as a classification model such as a multi-layer perception (MLP) or a support vector machine (SVM). -connected layer) and can be used for classification model learning and prediction.

However, the structure of the deep learning model 120 according to embodiments of the present invention is not limited to this, 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 the risk of developing a disease from the analysis server 100 and outputs it.

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 as separate devices.

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

In one embodiment, the input device 210 may receive blood from a user whose risk of developing a disease needs to be analyzed and calculate metabolic syndrome onset factors.

There may be one input device 210 or there may be multiple input devices 210.

The output device 220 may receive the disease development risk result from the analysis server 100 and provide the decision result to the user in various ways. For example, the output device 220 may include a display unit to visually display the risk of developing a disease and provide it to the user. Additionally, when receiving a determination result that the risk of developing a disease is high, the output device 220 may generate vibration to inform the user that the risk of developing a disease is high. However, the method by which the output device 220 provides the user with the determination result of the risk of developing a disease is not limited to this, and various output methods that can be provided to the user, such as audio output, can be utilized. Additionally, 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 there may be multiple output devices 220.

Hereinafter, a method for predicting the risk of developing a disease based on big data according to an embodiment of the present invention will be described. Below, the differences from the big data-based disease onset risk prediction system according to an embodiment of the present invention mentioned above will be described in detail, and parts not described will be explained in detail. It may be the same as the risk prediction system.

Figure 2 is a flowchart schematically showing a big data-based disease onset risk prediction method according to an embodiment of the present invention.

1 and 2, the big data-based disease onset risk prediction method according to an embodiment of the present invention includes the step of acquiring the input metabolic syndrome onset factor value for the metabolic syndrome onset factor (S100), big data It includes a collection step (S200) and a disease onset risk prediction step (S300).

Obtain the input metabolic syndrome onset factor value for the metabolic syndrome onset factor (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 into the input device 210 and provided to the analysis server 100.

Big data is formed by collecting input metabolic syndrome onset factor values over time (S200). The big data collection step (S200) may be performed in the analysis server 100.

By analyzing big data, the risk of developing a specific disease is predicted (S300). The risk of developing a disease may be provided from the analysis device 100 to the output device 220. The output device 220 and the input device 210 may be implemented as one device or may be implemented as separate devices.

In one embodiment, the disease onset risk prediction step (S300) is a learning step in which the input metabolic syndrome onset factor value and the disease onset risk of a specific disease are learned from a dataset by one or more computers using a deep learning learning model; And it may include predicting the risk of developing a disease using a deep learning model 120.

In one embodiment, the disease onset risk prediction step (S300) is a step of predicting the risk of metabolic syndrome including at least one of hypertension, dyslipidemia, and diabetes based on big data 110, and big data ( 110) and may include predicting the risk of chronic metabolic disease, including at least one of coronary artery disease, stroke, and chronic kidney disease, based on the predicted risk of metabolic syndrome.

In one embodiment, the disease onset risk prediction step (S300) is performed by one or more computers using a deep learning model to input metabolic syndrome onset factor values, metabolic syndrome onset risk, and disease onset risk of a specific disease into a dataset. It may include a learning step of learning, and a step of predicting the risk of developing the target syndrome and the risk of developing the disease using the deep learning learning model 120.

The risk of developing a disease can be determined by the parameters of the deep learning model 120 mentioned above.

In one embodiment, the learning step may include generating learning data by accumulating input metabolic syndrome onset factor values. Through analysis of learning data, normal metabolic syndrome onset factor values are calculated. It can be determined whether the input metabolic syndrome onset factor value exceeds a specific range formed based on the normal metabolic syndrome onset factor value.

In one embodiment, the learning step may include generating learning data by accumulating input metabolic syndrome onset factor values corresponding to tissues provided from one or more tissue analysis devices 200. Abnormal feature information can be extracted from learning data.

In one embodiment, the learning step may include matching specific situation data to abnormal feature information and accumulating it. Abnormal characteristic information may mean, for example, values that fall below the normal range based on abdominal circumference, blood pressure, blood sugar, HDL cholesterol, and triglyceride. Specific abnormal characteristic information can be recognized within the input metabolic syndrome onset factor value. Situation data corresponding to the recognized abnormal feature information can be transmitted to the client. For example, situational data corresponding to abnormal characteristic information may be the risk of developing a disease.

The big data-based disease onset risk prediction method according to an embodiment of the present invention is implemented as a program (or application) to be executed in conjunction with a computer, which is hardware, and is stored on a medium included in the big data-based disease onset risk prediction system. It can be saved.

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 in the program. (Code) may be included. These codes may include functional codes related to functions that define the necessary functions for executing methods, and may include control codes related to execution procedures necessary for the computer's processor to execute the functions according to predetermined procedures. . In addition, these codes may further include memory reference-related codes that indicate from which location (address address) in the computer's internal or external memory additional information or media required for the computer's processor to execute functions should be referenced. Additionally, if the computer's processor needs to communicate with any other remote computer or server to execute functions, how should the code communicate with any other remote computer or server using the computer's communication module? , It may further include communication-related codes regarding what information or media should be transmitted and received during communication.

A storage medium is not a medium that stores data for a short period of time, such as a register, cache, or memory, but a medium that stores data semi-permanently and can be read by a device. Specifically, examples of storage media include, but are not limited to, ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices. That is, the program can be stored in various recording media on various servers that the computer can access or in various recording media on the user's computer. Additionally, the medium may be distributed across computer systems connected to a network, and computer-readable code may be stored in a distributed manner.

The big data-based disease onset risk prediction system, prediction method, and program according to an embodiment of the present invention generate and analyze big data based on metabolic syndrome onset factors accumulated over time, By recognizing or predicting the level of risk, dangerous situations can be prevented in advance. The big data-based disease onset risk prediction system, prediction method, and program according to an embodiment of the present invention are based on big data generated based on metabolic syndrome onset factors accumulated over time, Risk can be recognized or predicted using deep learning technology.

Above, embodiments of the present invention have been described with reference to the attached drawings, but those skilled in the art will understand that the present invention can be implemented in other specific forms without changing its technical idea or essential features. You will understand that it exists. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive.

10: Big data-based disease onset risk prediction system
100: Analysis server
110: Big data
120: Deep learning learning model
200: Client
210: input device
220: output device

Claims (20)

It consists of one or more computers, collects input metabolic syndrome onset factor values for metabolic syndrome onset factors over time to form big data, and analyzes the big data to determine high blood pressure, dyslipidemia, diabetes, coronary artery disease, An analysis server that predicts the risk of developing certain diseases, including stroke and chronic kidney disease; and
Includes a client connected to the analysis server,
The client is
The analysis of the input metabolic syndrome onset factor value corresponding to the metabolic syndrome onset factor calculated through the blood of the user who needs to analyze the metabolic syndrome onset factor input through the input device and the disease onset risk provided through the input device. provided to the server,
Receiving the risk of developing the disease from the analysis server and printing it out,
The factors causing the metabolic syndrome are
Includes abdominal circumference, blood pressure, blood sugar, high-density lipoprotein (HDL) cholesterol, and triglycerides;
The analysis server is
If at least three of the input metabolic syndrome onset factor values exceed the reference value set based on the big data, predicting the risk of developing the disease for the specific disease,
Based on the big data, predict the risk of metabolic syndrome including the hypertension, dyslipidemia, and diabetes, and
A big data-based disease onset risk prediction system that predicts the risk of chronic metabolic diseases including the coronary artery disease, the stroke, and the chronic kidney disease, based on the big data and the predicted risk of metabolic syndrome. According to paragraph 1,
The analysis server is
Using a deep learning learning model, the input metabolic syndrome onset factor value and the disease onset risk of the specific disease are learned as a dataset,
A big data-based disease risk prediction system that predicts the risk of developing the disease using the deep learning model. According to paragraph 2,
The analysis server is
Generating learning data by accumulating the input metabolic syndrome onset factor values,
Through analysis of the learning data, the normal metabolic syndrome onset factor value in the normal range is calculated,
A big data-based disease onset risk prediction system that determines whether the input metabolic syndrome onset factor value exceeds a specific range formed based on the normal metabolic syndrome onset factor value. According to paragraph 2,
The analysis server is
Generating learning data by accumulating the input metabolic syndrome onset factor values,
A big data-based disease risk prediction system that extracts the disease risk from the learning data. According to paragraph 4,
The analysis server is
Accumulate specific situation data by matching it with abnormal feature information,
Recognize specific abnormal characteristic information within the input metabolic syndrome onset factor value,
A big data-based disease onset risk prediction system that transmits situational data corresponding to the recognized abnormal characteristic information to the client. According to paragraph 1,
The analysis server is
A big data-based disease risk prediction system that analyzes the big data using logistic regression analysis. delete delete According to paragraph 1,
The risk of developing the disease is
A big data-based disease onset risk prediction system that includes at least one of the onset time of the specific disease and the incidence of the specific disease. According to paragraph 1,
The client is
an input device that receives the input metabolic syndrome onset factor values and provides them to the analysis server; and
A big data-based disease onset risk prediction system including an output device that receives the disease onset risk from the analysis server and outputs it. Obtaining input metabolic syndrome onset factor values for metabolic syndrome onset factors input through an input device and metabolic syndrome onset factors calculated through the blood of a user who needs to analyze disease onset risk provided through the input device;
A big data collection step of collecting the input metabolic syndrome onset factor values over time to form big data; and
A disease onset risk prediction step of analyzing the big data to predict the risk of developing specific diseases including high blood pressure, dyslipidemia, diabetes, coronary artery disease, stroke, and chronic kidney disease,
The factors causing the metabolic syndrome are
Includes abdominal circumference, blood pressure, blood sugar, high-density lipoprotein (HDL) cholesterol, and triglycerides;
The stage of predicting the risk of developing the disease is
predicting the disease onset risk of the specific disease when at least three of the input metabolic syndrome onset factor values exceed a reference value set based on the big data;
Predicting the risk of metabolic syndrome including the high blood pressure, dyslipidemia, and diabetes based on the big data; and
Based on the big data and the predicted risk of metabolic syndrome, a big data-based disease development risk including predicting the risk of chronic metabolic diseases including the coronary artery disease, the stroke, and the chronic kidney disease. Prediction method. According to clause 11,
The stage of predicting the risk of developing the disease is
A learning step of learning the input metabolic syndrome onset factor value and the disease onset risk of the specific disease as a dataset using a deep learning learning model by one or more computers; and
A method for predicting the risk of developing a disease based on big data, including predicting the risk of developing the disease using the deep learning model. According to clause 12,
The learning step is
generating learning data by accumulating the input metabolic syndrome onset factor values;
calculating a normal metabolic syndrome onset factor value in a normal range through analysis of the learning data; and
A big data-based disease onset risk prediction method including; determining whether the input metabolic syndrome onset factor value exceeds a specific range formed based on the normal metabolic syndrome onset factor value. According to clause 12,
The learning step is
generating learning data by accumulating the input metabolic syndrome onset factor values; and
A big data-based disease onset risk prediction method comprising: extracting the disease onset risk from the learning data. According to clause 14,
The learning step is
A step of accumulating specific situation data by matching it with abnormal feature information;
Recognizing specific abnormal characteristic information within the input metabolic syndrome onset factor value; and
A big data-based disease onset risk prediction method further comprising: transmitting situation data corresponding to the recognized abnormal characteristic information to a client. According to clause 12,
The stage of predicting the risk of developing the disease is
A big data-based disease risk prediction method that analyzes the big data using logistic regression analysis. delete delete According to clause 12,
The risk of developing the disease is
A big data-based disease onset risk prediction method including at least one of the onset time of the specific disease and the incidence of the specific disease. A big data-based disease onset risk prediction program combined with a hardware computer and stored in a medium to execute the big data-based disease onset risk prediction method of any one of claims 11 to 16 and 19.