• Use Cases
  • /
  • Insurance
  • /
  • Health Insurance Cost Prediction

Health Insurance Cost Prediction

Health Insurance Cost Prediction


Health insurance is a type of insurance service that covers one's risk for medical expenses. The cost may vary depending on an individual's age, medical records, and other personal conditions. With access to data containing various personal information, it becomes possible to predict the cost of one's health insurance and find out which aspect has the biggest impact on it.

 - Predict insurance cost based on personal information data 
 - Discover which aspect has the biggest impact on insurance cost

Create Model

1. Check Data

 - This dataset is composed of age, gender, obesity, number of children, smoking status, region, and insurance cost of 1,339 individual patients.
 - The target variable 'charge' is a continuous value.

2. Build Model |  Create a health insurance cost prediction model using CLICK AI's structured data continuous classification

2.1 Model Development

[Main Page → Click on 'Create a new project.']

['Adding data' → Upload data file to Click AI.]

[Select uploaded data file from the dataset list → Click on 'Start AI.']

2.2 Model Configuration

[Select your preferred learning type, preferred method, and the value you would like to analyze/predict.]
 - The target variable 'charges' is a continuous value. Select 'Structured data continuous value classification' as your learning type.
 - To develop a model focused on maximizing its accuracy, select 'with higher accuracy' as your preferred way.
 - Set 'charges - insurance.csv' as your value you want to predict.
 - Click on (?) for more details about each selections.

2.3 Data Preprocessing

['Summary' → Decide which columns from the data set you would like to use to train the model.]
 - The columns will be automatically unchosen from being used as part of the training data.

[Data Preprocessing → Decide which columns you would like to preprocess.]
 - If you would like to preprocess data, continue by clicking on 'Preprocessing.'

[Choose preferred preprocessing methods and click 'confirm.']

['Data' → View your data file.]

2.4 Model Generation

[After all of the above steps are completed, press the 'Start' button to begin processing the AI model.]

[All of the processed models will appear in the list at the bottom of the page. You may sort the models according to RMSE, errorRate or Mase.]

3. View Details | Identify important factors for your model

 - Let's find out the factors that have the biggest impact on the predictions.

[Training Model 105 → Click on 'Details.']
 - View details of the [Training Model 105] with the lowest RMSE.

[Feature Importance → See which indices are important for the prediction model. Additional information are available throughout the different tabs.]

[ eXplainable AI ]
 - Smoking status of patients were analyzed to have the biggest impact on the insurance costs. Obesity and age also displayed significant impacts on the predictions.
 - You may explore through additional details and statistics on the 'Details' page.

[API Connection→ You can connect model results through API connections.]
  - API sources are available for multiple programming languages.

4. Analyze Data | Analyze features to derive insights from data

[Training Model 105 → Click on 'Analyze']
 - View data analysis of the [Training Model 105] with the lowest RMSE.

[AI+ Analysis]
 - The AI+ analysis feature provides analysis and visualization of the data, aimed to help  users better understand statistical information.
 - Top 5 indices highly associated with health insurance cost are organized for users.

[Visualized information such as histograms and distribution charts are available for users.]

5. Predict Data | Make single or multiple predictions using your model

5.1 Single Prediction
 - Predict health insurance costs for each individual customer's information.

[Training Model 105 → Click on 'Single prediction']
 - Make single predictions using the [Training Model 105] with the lowest RMSE.
 - 'Fill in random values' feature automatically fills all of the parameters randomly. After checking that all of the parameters have been filled appropriately, click 'Run' to proceed.

[The predicted result is displayed on the right.]
 - You may also manually enter values to make predictions.
 - If a value is out of range, the value will appear red.

5.2 Collective Prediction 
- Enter data to the downloaded collective prediction template, and run insurance cost predictions for multiple customers.

[Training Model 105 → Click on 'Collective prediction']
Make collective predictions using the [Training Model 105] with the lowest RMSE.

[Click on 'Download template for prediction' to download the prediction template.]

[Enter values to the prediction template (left), and prepare a prediction data set (right) to feed into the prediction model.]

[Upload the prepared prediction data, and proceed by pressing on 'Next'.]

[Prediction results will be available on a new csv file. The last column displays the prediction results for each patient information.]
 - Collective prediction may require some time. You can download the prediction results from the notifications tab after completion.

5.3 Auto-Labeling
 - Enter data to the downloaded auto-labeling template, and run insurance cost predictions for multiple customers.
 - Auto-labeling prediction will automatically preprocess data as needed.

[Training Model 105 → Click on 'Auto-labeling]
 - Make auto-labeling predictions using the [Training Model 105] with the lowest RMSE.

[Click on 'Download template for prediction' to download the prediction template.]

[Enter values to the prediction template (left), and prepare a prediction data set (right) to feed into the prediction model.]

[Upload the prepared prediction data, and proceed by pressing on 'Next'.]

[Prediction results will be available on a new csv file. The last column displays the prediction results for each patient information.]
 - Auto-labeling prediction may require some time to complete. You can download the prediction results from the notifications tab after completion.

6. Share | Share your AI model with API

[Training Model 105 → Click on 'Details']

[Sharing your service app]
 - The URL link will direct you to shared page of the model.
 - Share your model using the URL link.
 - Others may have access to your model through the URL link. If somebody uses the model to make predictions, it will be counted from your prediction count.
 - 'Analyze' feature offers both explainable AI and visualizations on the shared link.
 - You can put your AI model on sale by using the AI store. For details, please check AI 스토어.

Consult with an expert now.

Consult with an expert now.