A Shiny-powered R app that turns real-world diabetes health data into a gentle, intelligent health assistant. It uses a linear regression model to estimate glucose levels from user inputs — visualized beautifully with Plotly.

Smarter Insights, Healthier Living

• Predicts glucose levels based on user inputs like:

  • Pregnancies, Blood Pressure, Skin Thickness
  • Insulin, BMI, Diabetes Pedigree Function, Age

• Shows an interactive BMI vs Glucose plot, powered by Plotly

• Offers model insights that are simple to understand:

  • Key feature contributions
  • R² score to reflect how well it performs

• Clean, responsive UI built with Shiny + custom HTML/CSS

Language:
    R

Core Libraries:
    • shiny — app framework
    • dplyr — data manipulation
    • plotly — interactive visualization

Machine Learning Model:
    • Linear Regression (base lm() in R)

Interface & Styling:
    • Shiny UI with custom HTML & CSS

• Software:
  • R and RStudio
  • R packages: shiny, dplyr, plotly
• Dataset:
  • diabetes.csv with fields for Pregnancies, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, and Glucose.

git clone https://github.com/bhmuxkan/glucointel-ai-app.git
cd glucointel-ai-app

shiny::runApp('app.R')

install.packages(c("shiny", "dplyr", "plotly"))

1. Run the Application:

   shinyApp(ui = ui, server = server)

2. Input Data: Enter health details in the sidebar and hit "Predict Glucose Level".
3. View Prediction: The predicted glucose level will appear on the main panel, styled for clarity.

GlucoIntel started as a personal project — a quiet space to explore real healthcare data and build something that feels both meaningful and usable.

I wanted it to be more than just technically sound — something intuitive, thoughtful, and designed to help.

Through this project, I was able to:

• Work with real clinical datasets
• Build and interpret a clear machine learning model in R
• Design a soft, human-centered UI using Shiny
• Communicate results in a way that’s visual and accessible

This app reflects how I love to create: with clarity, care, and intention — blending data with design to make something gentle and empowering.

Contributions are welcome to help improve this project! You can:

1. Fork the Repository on GitHub.
2. Create a New Branch for your feature or bug fix:

   git checkout -b feature-name

3. Make Your Changes and commit:

   git commit -m "Add new feature or fix"

4. Push Changes to your branch:

   git push origin feature-name

5. Submit a Pull Request explaining the changes made.

We encourage you to suggest improvements, file issues, and help enhance this application!

If you love thoughtful UI, health tech, or just want to say hi — I’d love to connect.

• LinkedIn – Muskan

• This project is released under the MIT LICENSE
• Feel free to use, modify, and share — with kind attribution