HealthCore: Smart Community Health Monitoring and Early Warning System

🌐 Live Demo

Check out the deployed application:

healthcore.onrender.com

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💡 Project Overview

HealthCore is a Smart Health Surveillance and Early Warning System designed to detect, monitor, and help prevent outbreaks of water-borne diseases in vulnerable, remote communities, with a specific focus on the rural Northeastern Region (NER) of India.

Developed as a hackathon project, the system integrates community-level data collection, environmental monitoring, and Artificial Intelligence to provide real-time, actionable alerts to health officials.

Problem Statement

Water-borne diseases such as diarrhea, cholera, typhoid, and hepatitis A are common in rural and tribal areas of the NER, often linked to contaminated water and poor sanitation. The remote terrain and delayed medical response necessitate a proactive, digital solution to monitor and respond to emerging health threats in a timely manner.

✨ Key Features

HealthCore delivers a multi-faceted approach to public health monitoring:

1. AI-Driven Outbreak Prediction: Uses AI/ML models (developed in Jupyter Notebooks) to detect patterns and predict potential outbreaks based on reported symptoms, water quality reports, and seasonal trends.
2. Community-Level Data Collection: A mobile-friendly interface for local clinics, ASHA workers, and community volunteers to report health data via mobile apps or SMS.
3. Water Quality Integration: Designed to integrate with low-cost water testing kits or IoT sensors to monitor critical water source contamination parameters (e.g., turbidity, pH, bacterial presence).
4. Real-Time Alert System: Provides immediate alerts to district health officials and local governance bodies to mobilize rapid response teams.
5. Health Department Dashboard: Offers comprehensive dashboards for health officials to visualize hotspots, track intervention effectiveness, and allocate resources efficiently.
6. Accessibility Focus: Includes a multilingual mobile interface with support for offline functionality and tribal languages to maximize adoption in remote areas.

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💻 Technology Stack

Category	Technology	Notes
Backend	Python (Flask)	Main application logic and routing.
Data Science/AI	Jupyter Notebook	Used for AI/ML model development and analysis.
Frontend	HTML, CSS, JavaScript	User-facing interface and dashboards.
Database	SQLite	Lightweight, file-based database for portability (Migrated from MongoDB).
Deployment	Render	Platform used for the live demo deployment.

⚠️ Database Migration Notice

The project database has been migrated from MongoDB to SQLite.

Due to external connection issues, the project now utilizes SQLite as its database backend. This provides a lightweight, serverless solution perfect for development and small deployments.

Configuration: The database path is set in the .env file:

# .env
SQLITE_DB_PATH=healthcore.db

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⚙️ Local Setup and Installation

Follow these steps to get a local copy of HealthCore running on your machine.

Prerequisites

• Python 3.8+
• pip (Python package installer)

1. Clone the Repository

git clone https://github.com/VaibhavUPratap/HealthCore.git
cd HealthCore

2. Set up the Backend (Python)

Create and activate a virtual environment, then install the required Python packages:

# Create and activate environment (Linux/macOS)
python3 -m venv venv
source venv/bin/activate

# For Windows (PowerShell/CMD)
# python -m venv venv
# .\venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

3. Environment Configuration

Create a file named .env in the project root directory and add the database path configuration:

# .env
SQLITE_DB_PATH=healthcore.db

The healthcore.db file will be automatically created when the application is run.

4. Run the Application

Start the application using the runner script:

# Ensure your virtual environment is active
python run.py

The application should now be accessible in your web browser, typically at http://127.0.0.1:5000.

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🤝 Contributing

This was a hackathon project, and contributions are welcome to expand its features, improve the AI model, or enhance the UI/UX.

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add AmazingFeature details').
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 file for details.