IntegratedML-IRIS-Cloud-Height-prediction

Overview：
With the improvement of living standards, people pay more and more attention to physical health. And the healthy development of children has become more and more a topic of concern for parents. The child's physical development can be reflected from the child's height and weight. Therefore, it is of great significance to predict the height and weight in a timely manner. Pay attention to the child's developmental state through scientific prediction and comparison.
The project uses InterSystems IRIS Cloud SQL to support by entering a large number of weight and height related data, and establishes AutoML based on IntegratedML for predictive analysis. According to the input parent height, it can quickly predict the future height of children, and judge whether the child's body mass index is based on the current height and weight status. In the normal range.

**Key Applications: **InterSystems IRIS Cloud SQL, IntegratedML

Function：
By applying this program, the height of children in normal developmental state can be quickly predicted. Through the results, parents can judge whether the child's development is normal and whether clinical intervention is required, which will help to understand the child's future height; through the current weight status Determine whether the current child's BMI is normal and understand the child's current health status.

Application Scenario

1. Children's height prediction
2. Monitoring of child development

Program deployment：

Data Initialization

1. Create a table with sql statements

   create table IF NOT EXISTS MLTEST_MSG.HeightWeightPredictMSG (Id int primary key identity(101,2),number varchar(50), Height float, Weight  float);
   create table IF NOT EXISTS MLTEST_MSG.HeightWeightMSG (Id int primary key identity(101,2),number varchar(50), Height float, Weight  float);
   create table IF NOT EXISTS MLTEST_MSG.FamilyMSG(id int primary key identity(101,2),Csex int, Family float,Father float,Mother float, num int,Height float);
   create table IF NOT EXISTS MLTEST_MSG.FamilyPredictMSG(id int primary key identity(101,2),Csex int, Family float,Father float,Mother float, num int,Height float);
   

3. Upload the provided csv file to the cloud data platform, and import the data into the two tables as shown in the screenshot. Note that the field ID is removed when importing.
   
   
   

Front-end deployment

1. Notepad Open the config.js file under the distML folder, modify the information and start the front-end page (you can use Tomcat)

①: Backend path
②: When it is 1, the page is displayed in English, and when it is 0, it is Chinese.

Back-end deployment

Open the application.yml file in the backend package and modify the database information in the file

①: database url
②: Username
③: Password

Then start the automldemo1.jar package with the java command, the default port number is 9999

After starting, enter the front-end page (if you use tomcat to start the address: ip: port/distML)
The first time you enter the page, the model will be created and trained, and you need to wait for a while. You can also create a model with the following statement after the data initialization and table creation in step 1.

Create model FamiyModel predicting (Height) from MLTEST_MSG.FamilyMSG
Train model FamiyModel
Create model HeightWeightMODEL predicting (Height) WITH (Weight float)  from MLTEST_MSG.HeightWeightMSG
Train model HeightWeightMODEL

Test Flow

① Select the module
② Fill in the relevant data. If there is adult sibling data, you can click add to fill in the information.
③ Click Submit and wait for the prediction result to appear in a while.