# 🏥 Hospital Management System

## 📋 Overview

A comprehensive hospital management system built with Streamlit, featuring real-time analytics, medical image analysis, patient prediction, and multilingual support. The system includes advanced features like brain tumor detection, chatbot assistance, and dynamic dashboards.

## ⭐ Features

### 🔷 Core Functionalities

- **📊 Dynamic Dashboard**

  - Real-time patient flow monitoring

  - Interactive 3D data visualization

  - Department-wise statistics

  - Bed capacity tracking

  - Staff monitoring

### 🔬 Medical Analysis Tools

- **🧠 Brain Tumor Detection**

  - AI-powered tumor classification

  - Support for multiple tumor types (Pituitary, Meningioma, Glioma)

  - Real-time image processing

  - Confidence score display

- **🔍 Medical Image Analysis**

  - DICOM file support

  - Multiple format support (JPG, PNG)

  - Disease detection and classification

  - Automated reporting system

  - Visual annotations with confidence scores

### 👥 Patient Management

- **📈 Patient Prediction System**

  - Readmission risk analysis

  - Multiple factor consideration

  - Automated recommendations

  - Risk factor visualization

- **📊 Analytics Dashboard**

  - Patient flow trends

  - Department-wise statistics

  - Length of stay analysis

  - Interactive visualizations

  - Custom time period selection

### 🤝 Support Features

- **🤖 Hospital Assistant Chatbot**

  - Natural language processing

  - PDF/TXT export functionality

  - Chat history management

  - Real-time responses

- **🚨 Emergency Contact System**

  - Quick access to emergency services

  - Emergency alert submission

  - Location tracking

  - Priority-based routing

## 💻 Technical Requirements

### 📦 Dependencies

```python

streamlit

pandas

numpy

plotly

opencv-python

tensorflow

pillow

pydicom

google-cloud-aiplatform

ultralytics

fpdf

```

### ⚙️ Additional Requirements

- Python 3.8+

- CUDA-compatible GPU (for AI models)

- Minimum 8GB RAM

- 50GB storage space

## 🚀 Installation

1. Clone the repository:

```bash

git clone https://github.com/PIYUSH-JOSHI1/Readmission-Prediction.git

cd Readmission-Prediction

```

2. Install required packages:

```bash

pip install -r requirements.txt

```

3. Set up environment variables:

```bash

export GOOGLE_APPLICATION_CREDENTIALS="path/to/credentials.json"

export API_KEY="your-api-key"

```

4. Run the application:

```bash

streamlit run Hospital_Streamlit.py

```

## ⚙️ Configuration

### 🌐 Language Settings

The system supports multiple languages:

- 🇺🇸 English (default)

- 🇪🇸 Spanish

- 🇫🇷 French

Configure language settings in the settings menu.

### 🎨 Theme Configuration

Currently supports:

- 🌙 Dark theme (default)

Custom themes can be configured in `dark_theme` dictionary.

## 🔒 Security Features

- Secure file handling

- API key protection

- Session state management

- Secure data transmission

## 🤖 Model Information

### 🧠 Brain Tumor Detection Model

- Architecture: Custom CNN

- Input size: 224x224x3

- Output classes: 4 (Pituitary, No Tumor, Meningioma, Glioma)

### 🔍 Medical Image Analysis Model

- Framework: YOLO v8

- Supported formats: DICOM, JPG, PNG

- Real-time detection capabilities

## 👥 Contributing

1. Fork the repository

2. Create your feature branch (`git checkout -b feature/AmazingFeature`)

3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)

4. Push to the branch (`git push origin feature/AmazingFeature`)

5. Open a Pull Request

## 📄 License

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

## 🙏 Acknowledgments

- TensorFlow team for the deep learning framework

- Streamlit team for the web framework

- YOLO team for the object detection model

- Google Cloud team for the AI Platform services

## 💬 Support

For support, email: drigoon2512M@gmail.com or raise an issue in the repository.

## 🗺️ Roadmap

- [ ] Integration with Electronic Health Records

- [ ] Mobile application development

- [ ] Additional language support

- [ ] Advanced analytics features

- [ ] Real-time patient monitoring

- [ ] Integration with medical devices

## 🏗️ System Architecture

```

hospital-management-system/

├── main.py

├── models/

│   ├── keras_model.h5

│   └── yolov8n.pt

├── uploads/

├── static/

│   └── assets/

├── utils/

│   ├── image_processing.py

│   └── data_analysis.py

└── config/

    └── settings.py

```