DoctorAppointmentSystem

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DoctorAppointmentSystem

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This project focuses on developing a web-based Doctor-Patient Appointment System integrated with a disease prediction feature powered by machine learning

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# BookMyDoc - Doctor Appointment System

## Introduction

BookMyDoc is a web-based doctor appointment system designed to streamline medical consultation scheduling. It allows patients to book appointments with doctors, receive updates, and leverage machine learning for disease prediction based on symptoms.

- **Live Demo**: [BookMyDoc - Live](https://bookmydoc-five.vercel.app/) [Backend deployed on Render free tier, may take a moment to load initially]

---

## Key Features

- **User Authentication**: Secure login and signup using JWT with cookie expiration.
- **Doctor & Patient Selection**: Users can register as either a doctor or a patient.
- **Appointment Management**:
  - Doctors can view patient details (name, date, email, status: pending, confirmed, completed, or canceled).
  - Patients can request appointments and receive notifications.
  - Calendar view with different status colors for easy tracking.
- **Email Notifications**: Patients receive an email when a doctor confirms or cancels an appointment. The email includes the appointment status and a QR code for verification.
- **Verification Appointment**:
   - Patients receive an email with the approval or cancellation status and a QR code.
   - When both the doctor and patient meet, the doctor scans the QR code to verify the appointment.
- **Doctor Review**:
   - Patients receive an email link to rate their experience with a star rating and comments.
   - Reviews help future patients make informed decisions and assist doctors in improving service quality.
- **Disease Prediction**:
  - Three machine learning models implemented:
    - **General Disease Prediction**:
      - Uses symptoms to predict diseases.
      - Trained on **377 symptoms** and **200 diseases**.
      - Implements **Logistic Regression** with an **86% accuracy score**.
    - **Diabetes Prediction For Women**:
      - Uses patient details such as BMI, insulin, glucose levels, and pregnancy history.
      - Determines if a patient is diabetic (positive or negative result).
      - Implements **AdaBoost** with a **79% accuracy score**.
    - **Stroke Prediction**:
      - Uses features like gender, age, work type, and various health parameters.
      - Predicts whether a user is at risk of stroke.
      - Implements **Gradient Boosting** with a **91% accuracy score**.

---

- **Website Demo Video (Youtube link)**: <br> <br>
 [![YouTube](http://i.ytimg.com/vi/uO-ocNwpnIA/hqdefault.jpg)](https://www.youtube.com/watch?v=uO-ocNwpnIA)

---

## Technologies Used

### Frontend

- **HTML, CSS, Tailwind CSS** - Styling and UI components.
- **React.js** - Dynamic frontend framework for an interactive UI.
- **Deployed on Vercel**.

### Backend

- **Node.js & Express.js** - Server-side development.
- **MongoDB (Atlas)** - NoSQL database for storing user and appointment data.
- **JWT** - Secure authentication and session management.
- **Deployed on Render**.

### Machine Learning

- **Python & Flask** - API integration for disease prediction.
- **Scikit-learn** - Used for training and implementing ML models.
- **Models Implemented:**
  - **Logistic Regression**: General disease prediction.
  - **AdaBoost**: Diabetes prediction for women.
  - **Gradient Boosting**: Stroke prediction.
- **Deployed on Render**.

---

## How It Works

1. **User Registration & Login**
   - Sign up as a doctor or patient.
   - Authentication is handled using JWT for security.

2. **Appointment Booking & Management**
   - Patients browse and request appointments with doctors.
   - Doctors can accept or decline appointment requests.
   - Status updates (Pending, Confirmed, Completed, Canceled) are reflected in real time.
   - Calendar view displays appointment statuses for easy tracking.

3. **Verification Appointment**
   - Patients receive an email with the approval or cancellation status and a QR code.
   - When both the doctor and patient meet, the doctor scans the QR code to verify the appointment.

4. **Disease Prediction**
   - The main feature is disease prediction using symptoms.
   - Additionally, we have separate models for Diabetes Prediction (for women) and Stroke Prediction.

---

## Installation & Setup (run locally)

1. Clone the repository:
   ```bash
   git clone https://github.com/ParamPS25/DoctorAppointmentSystem.git
   cd DoctorAppointmentSystem
   ```

2. Set up Environment variables <br>

<h4>Frontend (.env)</h4> 
   Create a .env file in the frontend directory with the following variables: 
   
      ```bash
     VITE_ML_DOC_API = http://localhost:5000
     VITE_NODE_DOC_API = http://localhost:8080
     ```
   <h4> Backend (.env)</h4>
   Create a .env file in the backend directory with the following variables: 
   
      ```bash
     MONGO_URI = mongodb+srv://username:password@your-cluster.mongodb.net/your-database
     PORT = 8080
     JWT_ACCESS_SECRET = your_jwt_access_secret_key
     JWT_REFRESH_SECRET = your_jwt_refresh_secret_key
     JWT_EXPIRES_IN = 1h
     COOKIE_EXPIRES_IN = 24
     MAIL_PASS = your_email_app_password
     FRONTEND_URL=http://localhost:5173
      ```

3. Install dependencies for the frontend:
   ```bash
   cd frontend
   npm install
   npm run dev
   ```

4. Install dependencies for the backend:
   ```bash
   cd backend
   npm install
   npm run dev
   ```

5. Run the machine learning backend:
   ```bash
   cd ml-backend
   pip install -r requirements.txt
   python app.py
   ```

6. Using Conda for ML Backend (Alternative Setup):
   ```bash
    cd ml-backend
    conda create -n doc_env python=3.8 -y
    conda activate doc_env
    pip install -r requirements.txt
    python app.py
    ```

---

## Contributors

- **[Param Bhavasar](https://github.com/ParamPS25)** - Backend Development  
- **[Purv Patel](https://github.com/purvpatel123)** - Frontend Development  
- **[Prit Patel](https://github.com/Pritlimbani269)** - Frontend Development  
- **[Prince Patel](https://github.com/prince2004patel)** - Machine Learning

---

## License

- This project is licensed under the **MIT License**.

DoctorAppointmentSystem

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