🩺 Project: Healthcare Live Analytics

This project is a real-time analytics platform for patient health monitoring and risk prediction. The platform ingests, processes, and analyzes healthcare data to provide insights such as risk levels, real-time patient metrics, and future predictions for healthcare professionals.

📖 Features

• Data Ingestion: Real-time ingestion of patient data into a PostgreSQL database using Flask and Python.
• Risk Prediction: Machine learning models to predict patient health risk levels.
• Metrics Examples:
  • Real-time health risk scores for patients.
  • Historical trends in patient vitals (heart rate, blood pressure).
  • Aggregated insights for healthcare providers.
• Scalability: Built with Docker to handle large-scale data simulations.
• Future Plans:
  • Interactive dashboard for visualizing patient health trends.
  • Advanced machine learning models for more accurate predictions.
  • Integration with IoT devices for live data streaming.

🛠️ Technologies Used

• Languages: Python, SQL
• Database: PostgreSQL
• Libraries:
  • pandas: For data manipulation.
  • sqlalchemy: For database interaction.
  • scikit-learn: For building machine learning models.
  • dash: For creating interactive dashboards.
  • dash-bootstrap-components: For styling Dash components.
  • faker: For generating mock data.
  • numpy: For numerical computations.
  • joblib: For saving and loading machine learning models.
• Tools:
  • Docker: For containerized deployment.
  • GitHub: For version control.

📂 Directory Structure

healthcare_analytics/ ├── backend/ │ ├── app/ │ │ ├── pycache/ # Python cache files │ │ ├── dashboard.py # Dash-based visualization dashboard │ │ ├── generate_data.py # Script for generating mock data │ │ ├── main.py # Flask application entry point │ │ ├── requirements.txt # Python dependencies │ │ ├── model.pkl # Trained machine learning model │ ├── Dockerfile # Backend Dockerfile ├── database/ │ └── init.sql # Database schema initialization ├── kafka/ # Placeholder for Kafka configurations ├── scripts/ │ ├── data_preparation.py # Script for preparing training data │ ├── db.sh # Shell script for managing the database ├── .env # Environment variables ├── .gitignore # Git ignore file ├── docker-compose.yml # Docker Compose configuration └── README.md # Documentation for the project