Advanced Data Visualization for Diabetes Management

Advanced Data Visualization for Diabetes Management

Select a CSV or JSON file with data and follow the steps to visualize and predict diabetes outcomes.

User Manual for Advanced Data Visualization for Diabetes Management


This tool is designed to help diabetologists visualize patient data and predict diabetes outcomes using advanced machine learning techniques. It supports data input in CSV or JSON format and provides an interface for configuring a predictive model based on the data provided.


  • A modern web browser (Google Chrome, Firefox, Safari, etc.)
  • Patient data in CSV or JSON format

Getting Started

  1. Open the Tool: Launch the tool in your web browser.
  2. Load Your Data:
    • Click on the “Select a CSV or JSON file with data” input area.
    • Navigate to your data file on your computer and select it.
    • Click the “Load and Visualize Data” button to upload your data.

Configuring the Model

Once your data is loaded, you will proceed to configure the predictive model.

  1. Select Features and Labels:
    • Feature Columns: Select the columns from your data that you want the model to use as input features. Hold Ctrl (Cmd on Mac) to select multiple columns.
    • Label Column: Select the column that contains the outcome or label you want the model to predict.
  2. Set Model Parameters:
    • Number of Neurons per Layer: Enter the number of neurons (nodes) for each layer in the model. More neurons can capture complex patterns but may require more computational power.
    • Number of Layers: Enter the total number of layers in the model. Additional layers can help the model learn more complex relationships.
    • Dropout Rate: Enter the dropout rate to prevent overfitting. This is the fraction of neurons to drop out during training.
    • Number of Epochs: Set how many times the model should go through the training data. More epochs can lead to better training but can also cause overfitting.
  3. Train the Model:
    • After configuring the settings, click “Apply and Train Model.” The model will begin training using your specified settings.
    • Training progress will be displayed in the console of your browser (accessible typically via right-click -> Inspect -> Console).

Using the Predictions

After the model is trained, it will automatically apply the predictions to the data, and the results will be visualized in the LineUp visualization tool.

  1. Review Predictions:
    • Scroll through the visualized data in the LineUp container. You will see the original data along with a column of predicted outcomes labeled as “Predicted Label.”
  2. Export Predictions:
    • If you wish to save the predictions, click the “Export Results” button. This will download a CSV file containing the original data and the predictions, which you can then review or use as needed for further analysis or reporting.

Best Practices

  • Data Privacy: Ensure that all patient data is handled according to local regulations and privacy standards.
  • Model Validation: Regularly validate the model’s predictions against actual outcomes to ensure accuracy and reliability.
  • Continuous Learning: Update the model periodically with new data to improve its predictions and relevance.


For technical support or further assistance with the tool, contact the technical team or your IT support staff.

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