🎯 DiaDetect - Your Personal Diabetes Risk Predictor 🩺

DiaDetect is a sleek and functional web application designed to help users assess their risk of diabetes based on health metrics. Powered by Machine Learning and a simple, user-friendly Flask interface, it leverages a Support Vector Machine (SVM) model to predict diabetes likelihood. Just fill in your basic health parameters, and DiaDetect will analyze your risk of diabetes in an instant.

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🚀 Features

• 🧮 Machine Learning: Trained Support Vector Machine (SVM) model for accurate predictions.
• 🧑‍⚕️ Health Metrics: Input health data like BMI, glucose level, and more to assess diabetes risk.
• ⚡ Fast and Lightweight: Built using Flask, the app is fast and responsive.
• 🎨 Beautiful UI: A modern, mobile-responsive design powered by Bootstrap 4.
• 🔍 Explainability: Simple results that are easy to interpret for users with or without medical knowledge.

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🛠️ Technologies Used

Technology	Description
Python	Core programming language for backend logic and machine learning.
Flask	Lightweight web framework used to serve the application.
Scikit-learn	Python library for implementing the Support Vector Machine (SVM).
Bootstrap 4	Frontend framework for responsive design and UI components.
Joblib	Used for saving and loading the trained machine learning model.
HTML/CSS	Structure and design of the web interface.

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📊 Machine Learning Model

The diabetes prediction model is built using a Support Vector Machine (SVM). The following health metrics are used as features for prediction:

1. Pregnancies: Number of pregnancies.
2. Glucose: Plasma glucose concentration.
3. Blood Pressure: Diastolic blood pressure in mm Hg.
4. Skin Thickness: Triceps skinfold thickness in mm.
5. Insulin: 2-Hour serum insulin level (mu U/ml).
6. BMI: Body mass index (weight in kg/(height in m)^2).
7. Diabetes Pedigree Function: A function that scores the likelihood of diabetes based on family history.
8. Age: The age of the individual.

The model was trained using the Pima Indians Diabetes Database from the UCI Machine Learning Repository.

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🎨 User Interface

DiaDetect features a clean and modern interface, designed with Bootstrap 4. It’s mobile-friendly, ensuring users can interact with the app on any device.

• The form is straightforward, with health inputs clearly labeled.
• The prediction result is displayed in a friendly and informative format.

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⚙️ Installation and Setup

1. Clone the repository:

   git clone https://github.com/vishomallaoli/dia-detect.git
   cd dia-detect

2. Set up a virtual environment:

   python -m venv myenv
   source myenv/bin/activate  # On Windows use `myenv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Run the application:

   flask run

5. Open your browser and go to http://127.0.0.1:5000/.

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🧪 How to Use

1. Open the DiaDetect web interface.
2. Enter your health metrics such as Glucose, BMI, and Age.
3. Hit the "Predict" button.
4. The app will instantly display whether you're at risk for diabetes based on the input.

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📂 Project Structure

📁 dia-detect/
│
├── 📁 templates/          # HTML templates
│   └── index.html         # Main page for user input and result
│
├── app.py                 # Main Flask application
├── svm_model_8_features.joblib  # Pre-trained SVM model file
├── requirements.txt       # Python dependencies
├── README.md              # Project README (this file)
└── .gitignore             # Ignored files and directories

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👥 Contributors

• Visho Malla Oli - Project Lead & Developer
  GitHub Profile

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📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🛠️ Future Enhancements

• More Features: Incorporate additional health metrics to improve accuracy.
• Graphical Output: Display risk results with visual aids like charts or graphs.
• User Accounts: Allow users to save and track their predictions over time.

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🌟 Show Your Support

If you found this project helpful, consider giving it a ⭐️ on GitHub!

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1. **Fancy Design Elements**:
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2. **Functional and Descriptive**:
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3. **Visual and User-Friendly**:
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