阅读说明：本技术 人体健康状况分级方法及装置 (Human health condition grading method and device ) 是由 洪卓辉 于 2021-09-16 设计创作，主要内容包括：本申请提供了一种人体健康状况分级方法及装置,可用于金融领域或其他领域,所述人体健康状况分级方法,包括：获取目标人群的年龄数据和身体质量指数；根据所述年龄数据、身体质量指数和预设的聚类模型,对所述目标人群进行聚类；根据所述目标人群的聚类结果,确定各类人群各自对应的身体健康状况等级。本申请能够提高人体健康状况评价的准确性,以便及时干预和指导,保证人员的健康。(The application provides a method and a device for grading human health conditions, which can be used in the financial field or other fields, wherein the method for grading the human health conditions comprises the following steps: acquiring age data and body mass index of a target population; clustering the target population according to the age data, the body mass index and a preset clustering model; and determining the respective corresponding body health condition grades of various groups according to the clustering result of the target group. The method and the device can improve the accuracy of the evaluation of the health condition of the human body, so that timely intervention and guidance can be realized, and the health of personnel can be guaranteed.)

1. A method of grading a health condition of a human, comprising:

acquiring age data and body mass index of a target population;

clustering the target population according to the age data, the body mass index and a preset clustering model;

and determining the respective corresponding body health condition grades of various groups according to the clustering result of the target group.

2. The method of claim 1, wherein the clustering model is pre-trained based on the MeanShift algorithm.

3. The method of claim 1, further comprising, after clustering the target population according to the age data, the body mass index and a preset clustering model:

if the cluster number in the clustering result is larger than the cluster number threshold value, acquiring a clustering center point, an age mean value and a body mass index mean value of each cluster;

and outputting and displaying the cluster center point, the age mean and the body mass index mean of each cluster.

4. The method of claim 1, further comprising, after clustering the target population according to the age data, the body mass index and a preset clustering model:

outputting and displaying the clustering result in a clustering map form;

and points in the clustering graph represent personnel in the target crowd, and a connecting line between the two points represents the distance from the personnel to the clustering center of the cluster where the personnel are located.

5. A human health grading apparatus, comprising:

the acquisition module is used for acquiring age data and body mass indexes of target people;

the clustering module is used for clustering the target population according to the age data, the body quality index and a preset clustering model;

and the grading module is used for determining the respective corresponding body health condition grades of various crowds according to the clustering result of the target crowd.

6. The device according to claim 5, wherein the clustering model is pre-trained based on the MeanShift algorithm.

7. The human health grading device of claim 5, further comprising:

the judging module is used for acquiring a clustering center point, an age mean value and a body mass index mean value of each cluster if the number of clusters in the clustering result is greater than a cluster number threshold value;

and the output module is used for outputting and displaying the cluster center point, the age mean value and the body mass index mean value of each cluster.

8. The human health grading device of claim 5, further comprising:

the map output module is used for outputting and displaying the clustering result in a clustering map form;

and points in the clustering graph represent personnel in the target crowd, and a connecting line between the two points represents the distance from the personnel to the clustering center of the cluster where the personnel are located.

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of classifying a health condition of a human as claimed in any one of claims 1 to 4 when executing the program.

10. A computer readable storage medium having computer instructions stored thereon, wherein the instructions, when executed, implement the method of grading a health condition of a human as claimed in any one of claims 1 to 4.

Technical Field

The application relates to the technical field of data processing, in particular to a human health condition grading method and device.

Background

Body Mass Index (BMI) is a closely related Index to the total Body fat, and mainly reflects the general overweight and obesity. The BMI index is equal to the weight (KG) divided by the height (M) squared, i.e., BMI is kilograms per square meter.

At present, it is generally considered that if the body mass index is < 18.5, the human health condition is: thinning; if the body mass index is in the range of 18.5 to 23.9, the human health condition is: normal; if the body mass index is more than or equal to 24, the health condition of the human body is as follows: being overweight; if the body mass index is between 24 and 26.9, the human health condition is: partial obesity; if the body mass index is 27 to 29.9, the human health condition is: obesity; if the body mass index is more than or equal to 30, the health condition of the human body is as follows: severe obesity; if the body mass index is more than or equal to 40, the health condition of the human body is as follows: extremely severe obesity.

However, in practice, the physical quality indexes of different people are the same, and the physical health conditions are not necessarily the same, and it is difficult to comprehensively and accurately evaluate the health conditions of human bodies only by the physical quality indexes.

Disclosure of Invention

Aiming at the problems in the prior art, the application provides a human health status grading method and device, which can improve the accuracy of human health status evaluation so as to intervene and guide in time and ensure the health of personnel.

In order to solve the technical problem, the present application provides the following technical solutions:

in a first aspect, the present application provides a method for grading a health condition of a human body, comprising:

acquiring age data and body mass index of a target population;

clustering the target population according to the age data, the body mass index and a preset clustering model;

and determining the respective corresponding body health condition grades of various groups according to the clustering result of the target group.

Further, the clustering model is obtained by pre-training based on the MeanShift algorithm.

Further, after the clustering the target population according to the age data, the body mass index and a preset clustering model, the method further comprises:

if the cluster number in the clustering result is larger than the cluster number threshold value, acquiring a clustering center point, an age mean value and a body mass index mean value of each cluster;

and outputting and displaying the cluster center point, the age mean and the body mass index mean of each cluster.

Further, after the clustering the target population according to the age data, the body mass index and a preset clustering model, the method further comprises:

outputting and displaying the clustering result in a clustering map form;

and points in the clustering graph represent personnel in the target crowd, and a connecting line between the two points represents the distance from the personnel to the clustering center of the cluster where the personnel are located.

In a second aspect, the present application provides a human health grading device comprising:

the acquisition module is used for acquiring age data and body mass indexes of target people;

the clustering module is used for clustering the target population according to the age data, the body quality index and a preset clustering model;

and the grading module is used for determining the respective corresponding body health condition grades of various crowds according to the clustering result of the target crowd.

Further, the clustering model is obtained by pre-training based on the MeanShift algorithm.

Further, the human health status grading device further comprises:

the judging module is used for acquiring a clustering center point, an age mean value and a body mass index mean value of each cluster if the number of clusters in the clustering result is greater than a cluster number threshold value;

and the output module is used for outputting and displaying the cluster center point, the age mean value and the body mass index mean value of each cluster.

Further, the human health status grading device further comprises:

the map output module is used for outputting and displaying the clustering result in a clustering map form;

and points in the clustering graph represent personnel in the target crowd, and a connecting line between the two points represents the distance from the personnel to the clustering center of the cluster where the personnel are located.

In a third aspect, the present application provides an electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method for classifying a health status of a human body.

In a fourth aspect, the present application provides a computer readable storage medium having stored thereon computer instructions that, when executed, implement the method for grading a health condition of a human.

According to the technical scheme, the application provides a method and a device for grading human health conditions. Wherein, the method comprises the following steps: acquiring age data and body mass index of a target population; clustering the target population according to the age data, the body mass index and a preset clustering model; according to the clustering result of the target crowd, determining the body health condition grades corresponding to various crowds respectively, and improving the accuracy of evaluating the body health condition so as to intervene and guide in time and ensure the health of personnel; specifically, by clustering target crowds, corresponding motion guidance schemes can be provided for various crowds more accurately and effectively; the intelligent degree of the human health condition evaluation can be improved, the labor cost is saved, and the evaluation efficiency can be improved on the basis of ensuring the accuracy of the human health condition evaluation.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a schematic flow chart of a method for grading a health condition of a human in an embodiment of the present application;

FIG. 2 is a schematic diagram of a clustering graph in one example of the present application;

FIG. 3 is a schematic flow chart of a method for grading a health status of a person according to another embodiment of the present application;

FIG. 4 is a schematic structural diagram of a human health grading device in the embodiment of the present application;

FIG. 5 is a schematic diagram of a human health grading device in another embodiment of the present application;

fig. 6 is a schematic block diagram of a system configuration of an electronic device according to an embodiment of the present application.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Currently, when evaluating the health condition of a human body, the total amount of body fat is generally evaluated based on a body mass index; however, in practice, the BMI of people under 60 years of age generally remains between 18.5 and 23.9, with fat levels in the normal range; after the age of 60, the BMI of the population is between 24 and 29.9, and the fat level is in a normal range. Therefore, there is a problem that the accuracy is low when the human health condition is evaluated only by the body quality index.

Based on this, in order to improve the accuracy of human health condition evaluation, so as to intervene and guide in time and ensure the health of personnel, the embodiment of the present application provides a human health condition classification apparatus, which may be a server or a client device, where the client device may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), a vehicle-mounted device, an intelligent wearable device, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch and intelligent bracelet etc..

In practical applications, the human health status grading part may be performed on the server side as described above, or all operations may be performed in the client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. The client device may further include a processor if all operations are performed in the client device.

The client device may have a communication module (i.e., a communication unit), and may be communicatively connected to a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.

The server and the client device may communicate using any suitable network protocol, including network protocols not yet developed at the filing date of this application. The network protocol may include, for example, a TCP/IP protocol, a UDP/IP protocol, an HTTP protocol, an HTTPS protocol, or the like. Of course, the network Protocol may also include, for example, an RPC Protocol (Remote Procedure Call Protocol), a REST Protocol (Representational State Transfer Protocol), and the like used above the above Protocol.

It should be noted that the method and the device for grading the human health condition disclosed in the present application can be used in the field of financial technology, and can also be used in any field other than the field of financial technology.

The following examples are intended to illustrate the details.

In order to improve the accuracy of evaluating the human health status, so as to intervene and guide in time and ensure the health of people, the present embodiment provides a human health status grading method, in which the execution subject is a human health status grading device, the human health status grading device includes but is not limited to a server, as shown in fig. 1, and the method specifically includes the following contents:

step 100: and acquiring age data and body mass index of the target population.

Specifically, the age data and the body mass index of each person in the target population can be obtained, and the BMI (BMI) is kilogram per square meter; the target population can be set according to actual needs, but is not limited in this application, for example, a worker in a certain unit, a resident in a certain area, a teenager in 15 to 25 years old in a certain province and university, and the like.

Step 200: and clustering the target population according to the age data, the body quality index and a preset clustering model.

Specifically, after the data are standardized, clustering analysis can be performed on the age and BMI data by using a clustering algorithm, and then a result is output; the algorithm can run independently under python.

Step 300: and determining the respective corresponding body health condition grades of various groups according to the clustering result of the target group.

Specifically, the target population can be divided into a plurality of groups by clustering, wherein each group corresponds to one type of population; the clustering result can be output and displayed; receiving the body health condition grades which are returned by the user at the front end and correspond to various crowds respectively; the physical health status of the crowd can be evaluated according to the corresponding physical health status grade of each crowd, for example, the higher the physical health grade is, the better the health status of the crowd is, so that the corresponding motion guidance scheme can be provided for the crowd more accurately and effectively.

To further illustrate the present solution, the present application provides an application example of a human health status grading method, which specifically includes:

s1: raw data were extracted from Excel as "D: \ \ python _ TEST \ \ AP _ TEST \ \ data. csv ", for data loading, two columns of 'age' and 'BMI' were selected for data analysis.

S2: a function is defined to complete the cluster analysis (main routine).

S3: two parameters are defined: one for clustering algorithms and one for raw data; the method comprises the steps of performing clustering analysis by using a MeanShift algorithm, filling missing values of original data with average values, standardizing the original data, calling the MeanShift algorithm, performing bandwidth calculation by using the MeanShift algorithm, training data by using the MeanShift algorithm, obtaining labels, clustering centers and cluster numbers of clustering clusters, printing cluster numbers and contour coefficients, wherein the larger the contour coefficient value is, the better the relative clustering effect is, the more reasonable the obtained classification result is, and the clustering performance is evaluated.

S4: outputting a clustering result and optimizing a visual result, describing the clustering result by using a clustering map, wherein each clustering cluster corresponds to one color, and the center of the clustering cluster is represented by a large circle.

Optimizing a visual result, and applying two layers of nested loops, wherein the first layer of loops draws the distribution of each cluster, and the second layer of loops further draws connecting lines from all points in each cluster to a cluster center on the basis that the first layer of loops draws the distribution of each cluster.

Specifically, the first "for loop" is used to obtain the current clustering label, obtain the center of the current clustering, draw the point of the current clustering, draw the center point of the current clustering, and the second "for loop" is used to visualize the result of each clustering, and draw the connecting line between the regular point and the center point.

S5: and printing an analysis report, and calculating and outputting a detailed report of the result meeting the condition after the analysis of the cluster and the output of the result are completed. Print the number of cases per cluster, the raw data with tags, the cluster center of the raw data, and the average for each cluster.

In one example, the code segments for obtaining the clustering results are as follows:

#Call the main programme for application

dt＝original_data[['AGE'，'BMI']]#Loading data

cluster_demo_(0.20，dt)#Run the programme

the code is API calling code, firstly assigning raw data of 'age' and 'BMI' for analysis to a specified variable 'dt', then calling a pre-compiled clustering program named 'cluster _ demo _', wherein the program is written by applying python function, and the program comprises two function parameters, wherein one function parameter is 'qtl', the core parameter 'quantile' for passing Meanshift clustering algorithm is used for determining granularity of clustering, and the other parameter is 'orgnl _ data', the raw data for specified analysis is transmitted, and the parameter is a specific parameter written in 'cluster _ demo _'. For example, run: cluster _ demo _ (0.20, dt), "cluster _ demo" will automatically run the cluster analysis on the raw data by passing qtl ═ 0.20 and orgnl _ data ═ dt, and output the cluster result of the MeanShift algorithm based on quantile ═ 0.20.

The clustering result may be as follows:

the number of clusters: 3 (in category 3);

contour coefficients (Silhouette scores): 0.333 (contour coefficient, representing the accuracy of clustering, between 0-1); 0b (cluster center point of first class is): [ 0.396362910.05364562 ]; 1g (cluster center point of the second class is): [ -1.265112221.56014565 ]; 2r (cluster center point of the third class): [ -0.549044492.8840063], as shown in fig. 2, a clustering graph can be obtained, and three colors can be used to represent 3 categories, and 3 large circles represent the center points of three clusters.

The detailed analysis report may contain raw data, cluster center values, and mean values of age data and body mass index in each cluster.

Number of cases in each cluster: ({0:82,1:19,2:3}).

The raw data may be as shown in table 1:

TABLE 1

	Age (age)	BMI	Class (specific classification for each case)
				0	45	24.203000	0
1	58	24.892167	0
				2	65	23.233000	0
3	62	29.843000	0
				4	42	27.217000	1
……	……	……	……
				99	65	30.471000	0
100	37	30.104000	1
				101	58	21.094000	0
102	76	27.888000	0
				103	63	30.488000	0

The age and BMI values for each cluster center point can be as shown in table 2:

TABLE 2

Cluster ID	Age (age)	BMI	Class
				0	45	24.203000	0
1	58	24.892167	1
				2	65	23.233000	2

The average values for age and BMI for each cluster can be shown in table 3:

TABLE 3

Class	Age (age)	BMI
			0	59.768293	23.675098
1	35.631579	28.527491
			2	44.666667	35.135000

In order to further improve the reliability of the clustering model and further improve the accuracy of the body health condition classification by applying the reliable clustering model, the clustering model can be obtained by pre-training based on the MeanShift algorithm.

Specifically, the MeanShift algorithm is one of SCIKIT-Learn machine learning clustering algorithms, belongs to an unsupervised algorithm, and can perform active self-learning and training.

In order to better adapt to the application rationality of the actual application and realize the visual classification evaluation, referring to fig. 3, in an embodiment of the present application, after step 200, the method further includes:

step 400: and if the cluster number in the clustering result is greater than the cluster number threshold value, acquiring the clustering center point, the age mean value and the body mass index mean value of each cluster.

Specifically, the cluster center point may be equivalent to a standard value within the present cluster.

Step 500: and outputting and displaying the cluster center point, the age mean and the body mass index mean of each cluster.

For example, if the number of clusters is greater than or equal to 3, a cluster analysis report may be output, where the cluster analysis report may include: raw data, cluster center value, and mean of age data and body mass index in each cluster.

In order to improve the visualization degree of the clustering result, and visually display the clustering result, in an embodiment of the present application, after step 200, the method further includes:

step 600: outputting and displaying the clustering result in a clustering map form; and points in the clustering graph represent personnel in the target crowd, and a connecting line between the two points represents the distance from the personnel to the clustering center of the cluster where the personnel are located.

Specifically, how much each individual within the cluster differs from a standard value can be measured by applying the cluster map, thereby giving each individual an accurate evaluation.

In terms of software, in order to improve the accuracy of evaluating the human health condition, so as to intervene and guide in time and ensure the health of people, the present application provides an embodiment of a human health condition classification apparatus for implementing all or part of the contents in the human health condition classification method, and referring to fig. 4, the human health condition classification apparatus specifically includes the following contents:

and the acquisition module 01 is used for acquiring age data and body mass indexes of the target population.

And the clustering module 02 is used for clustering the target population according to the age data, the body quality index and a preset clustering model.

And the grading module 03 is configured to determine the respective body health status grades of various groups of people according to the clustering result of the target group.

Specifically, the clustering model may be pre-trained based on the MeanShift algorithm.

Referring to fig. 5, in an embodiment of the present application, the apparatus for grading a health condition of a human body further includes:

and the judging module 04 is configured to obtain a clustering center point, an age average value and a body mass index average value of each cluster if the number of clusters in the clustering result is greater than the cluster number threshold.

And the output module 05 is used for outputting and displaying the cluster center point, the age mean value and the body mass index mean value of each cluster.

In an embodiment of the present application, the apparatus for grading a health condition of a human body further includes:

and the map output module is used for outputting and displaying the clustering result in a clustering map form.

And points in the clustering graph represent personnel in the target crowd, and a connecting line between the two points represents the distance from the personnel to the clustering center of the cluster where the personnel are located.

The embodiment of the human health status grading device provided in this specification may be specifically configured to execute the processing procedure of the embodiment of the human health status grading method, and the functions of the embodiment of the human health status grading device are not described herein again, and reference may be made to the detailed description of the embodiment of the human health status grading method.

According to the description, the method and the device for grading the human health condition can improve the accuracy of evaluation of the human health condition so as to intervene and guide in time and ensure the health of personnel; specifically, the intelligent degree of human health condition evaluation can be improved, the labor cost is saved, and the evaluation efficiency can be improved on the basis of ensuring the accuracy of human health condition evaluation.

In terms of hardware, in order to improve the accuracy of evaluating the human health condition, so as to intervene and guide in time and ensure the health of people, the present application provides an embodiment of an electronic device for implementing all or part of the contents in the method for grading the human health condition, where the electronic device specifically includes the following contents:

a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission among the human health status grading device, the user terminal and other related equipment; the electronic device may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the electronic device may be implemented with reference to the embodiment for implementing the method for classifying the human health status and the embodiment for implementing the apparatus for classifying the human health status, which are incorporated herein, and repeated details are not repeated.

Fig. 6 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 6, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this FIG. 6 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.

In one or more embodiments of the present application, the human health grading function can be integrated into the central processor 9100. The central processor 9100 may be configured to control as follows:

step 100: and acquiring age data and body mass index of the target population.

Step 200: and clustering the target population according to the age data, the body quality index and a preset clustering model.

Step 300: and determining the respective corresponding body health condition grades of various groups according to the clustering result of the target group.

From the above description, the electronic device provided by the embodiment of the application can improve the accuracy of human health condition evaluation, so as to intervene and guide in time and ensure the health of people.

In another embodiment, the human health grading device may be configured separately from the central processor 9100, for example, the human health grading device may be configured as a chip connected to the central processor 9100, and the human health grading function is realized by the control of the central processor.

As shown in fig. 6, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 6; further, the electronic device 9600 may further include components not shown in fig. 6, which may be referred to in the art.

As shown in fig. 6, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.

The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.

The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.

The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.

The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).

The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.

Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.

The above description shows that the electronic device provided by the embodiment of the application can improve the accuracy of human health condition evaluation, so as to intervene and guide in time and ensure the health of people.

Embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps in the method for classifying a human health condition in the above embodiments, where the computer-readable storage medium stores thereon a computer program, and when the computer program is executed by a processor, the computer program implements all steps of the method for classifying a human health condition in the above embodiments, for example, the processor implements the following steps when executing the computer program:

step 100: and acquiring age data and body mass index of the target population.

Step 200: and clustering the target population according to the age data, the body quality index and a preset clustering model.

Step 300: and determining the respective corresponding body health condition grades of various groups according to the clustering result of the target group.

From the above description, it can be known that the computer-readable storage medium provided in the embodiments of the present application can improve the accuracy of human health status evaluation, so as to intervene and guide in time and ensure the health of people.

In the present application, each embodiment of the method is described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. Reference is made to the description of the method embodiments.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The principle and the implementation mode of the present application are explained by applying specific embodiments in the present application, and the description of the above embodiments is only used to help understanding the method and the core idea of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.