🏥 EquiHealth - Healthcare Analytics Platform

Empowering data-driven healthcare decisions through advanced analytics and visualization

EquiHealth is a comprehensive web-based healthcare analytics platform that leverages machine learning to analyze health metrics, predict outcomes, and visualize healthcare accessibility across different regions. The platform enables healthcare professionals, policymakers, and researchers to make informed decisions based on comprehensive data analysis.

🚀 Deployment

🌐 Live Demo: https://equihealth.onrender.com

🌟 Features

📊 Data Analytics & Visualization

Interactive Data Upload: Support for CSV files with flexible column mapping
Real-time Data Processing: Instant analysis of healthcare metrics
Interactive Geographic Visualization: Plotly-powered maps with hover functionality and zoom capabilities
User-Specific Data Management: Session-based data isolation for multi-user support
Comprehensive Reports: Detailed analytics with downloadable results and scrollable data tables

🤖 Machine Learning Capabilities

Predictive Modeling: XGBoost-powered health outcome predictions with hyperparameter optimization
Risk Assessment: Advanced algorithms for healthcare risk evaluation
Data Standardization: Automated preprocessing and feature scaling with StandardScaler
Model Persistence: Trained models saved for consistent predictions using joblib serialization
Real-time Predictions: Live model inference on uploaded healthcare data

🎨 Modern User Interface

Responsive Design: Optimized for desktop, tablet, and mobile devices
Glassmorphism UI: Modern, professional interface with backdrop blur effects and transparent overlays
Intuitive Navigation: Fixed navigation bars with context-aware links and smooth transitions
Session-Based Authentication: Secure user login/signup with password hashing and session management
Flash Message System: Real-time user feedback with animated notifications
Accessibility: High contrast design with enhanced readability and ARIA compliance

📈 Healthcare Metrics Analysis

Disease Tracking: TB, Diabetes, Malaria, HIV/AIDS incidence analysis
Public Health Indicators: Infant Mortality Rate (IMR) monitoring
Vaccination Coverage: Immunization rate tracking and analysis
Socioeconomic Factors: Income level correlation with health outcomes

🚀 Getting Started

Prerequisites

Python 3.8+
pip (Python package installer)
Modern web browser (Chrome, Firefox, Safari, Edge)

Installation

Clone the repository

git clone https://github.com/MaheshR03/equihealth.git
cd equihealth

Install dependencies
```
pip install -r requirements.txt
```
Run the application
```
python app.py
```
Access the platform Open your browser and navigate to http://localhost:5000

📸 Application Screenshots

🏠 Landing Page

Modern glassmorphism design with intuitive navigation

📊 Data Upload Interface

Intuitive CSV upload with intelligent column mapping

📈 Analytics Dashboard

Comprehensive data analysis and results

🗺️ Interactive Maps

Plotly-powered geographic visualizations with hover functionality

📚 Resources & About

External resources and platform information

🛠️ Tech Stack

Backend Technologies

Flask - Lightweight WSGI web application framework
Python - Core programming language
Pandas - Data manipulation and analysis
NumPy - Numerical computing library
Scikit-learn - Machine learning utilities and preprocessing

Machine Learning

XGBoost - Gradient boosting framework for predictive modeling
StandardScaler - Feature normalization and standardization
Pickle - Model serialization and persistence

Data Visualization

Plotly Integration - Interactive maps with hover tooltips and zoom functionality
HTML Map Generation - Dynamic visualization files for user-specific data
Responsive Iframes - Seamless integration of interactive content
Color-coded Visualizations - Intuitive data representation with prediction-based coloring

Frontend Technologies

HTML5 - Semantic markup language with modern form elements
CSS3 - Advanced styling with glassmorphism effects, animations, and responsive grids
JavaScript - Interactive user interface elements, form validation, and enhanced UX
Responsive Design - Mobile-first approach with flexible layouts and adaptive navigation

Development Tools

Flask Debug Mode - Development server with hot reload and error tracking
CSV Processing - Flexible data import capabilities with intelligent column mapping
Static File Serving - Efficient asset delivery with proper MIME type handling
Session Management - Secure user authentication with Werkzeug password hashing
Interactive Maps - Plotly-powered visualizations with user-specific file generation

📋 Usage Guide

1. User Authentication

Create an account or login to access personalized features
Secure session management ensures data privacy and user isolation
Password protection with industry-standard hashing

2. Data Upload

Navigate to the Upload page (authentication required)
Prepare your CSV file with required healthcare columns:
- District, Latitude, Longitude
- TB Incidence, Diabetes Prevalence
- Malaria Incidence, HIV/AIDS Prevalence
- IMR, Vaccination Rate, Income Level
Upload your CSV file for real-time analysis and ML predictions

3. View Results

Access the Report page to view processed data in interactive tables
Download analysis results in CSV format with user-specific predictions
Review ML-generated scores and comprehensive health metrics

4. Interactive Geographic Analysis

Visit the Map page for dynamic visual representation
Hover over data points to see detailed district information and predictions
Zoom and pan through interactive maps powered by Plotly
Analyze healthcare accessibility patterns across regions

5. Resource Access

Browse the Resources page for external health databases
Access WHO, CDC, and Indian health ministry data
Find relevant research and policy documents

🏗️ Project Structure

equihealth/
├── app.py                 # Main Flask application with authentication & ML integration
├── requirements.txt       # Python dependencies (updated with Plotly)
├── README.md             # Comprehensive project documentation
├── health_data.csv       # Sample healthcare dataset
├── predictions.csv       # ML model output
├── test_upload.csv       # Sample test data for demonstrations
├── health-data/          # Model storage directory
│   ├── predictionmodel.pkl # Trained XGBoost model
│   └── scaler.pkl        # StandardScaler for feature normalization
├── static/               # Static assets
│   ├── styles.css        # Enhanced stylesheet with glassmorphism design
│   ├── scripts.js        # JavaScript for interactive UX features
│   ├── health_data.csv   # Sample data for download
│   └── maps/             # User-specific interactive map storage
│       └── *.html        # Plotly-generated interactive maps per user
├── templates/            # HTML templates with unified navigation
│   ├── frontpage.html    # Modern landing page
│   ├── login.html        # User authentication interface
│   ├── signup.html       # User registration interface
│   ├── upload.html       # Data upload interface with validation
│   ├── report.html       # Analysis results with scrollable tables
│   ├── result.html       # Prediction results with professional styling
│   ├── show-map.html     # Interactive geographic visualization
│   ├── resource.html     # External resources and links
│   └── about.html        # Platform information and features
└── healthcare_access_xgboost_model.ipynb     # ML model development notebook (XGBoost training)

🔬 Machine Learning Pipeline

Data Preprocessing

Column Standardization: Flexible mapping of CSV columns to expected features with intelligent name recognition
Missing Value Handling: Intelligent imputation for incomplete data with validation checks
Feature Scaling: StandardScaler normalization for optimal model performance and consistency
Data Validation: Comprehensive checks for data integrity and user-friendly error messages
User Data Isolation: Session-based data management ensuring privacy and multi-user support

Model Architecture

Algorithm: XGBoost Classifier with hyperparameter optimization using RandomizedSearchCV
Training Pipeline: SMOTE-ENN for class balancing and robust model training
Feature Engineering: Automated creation of derived health indicators and geographic features
Cross-validation: 5-fold validation ensures model generalization and reliability
Model Persistence: Joblib serialization for consistent inference across sessions

Prediction Output

Health Risk Classifications: Multi-class predictions (Very Low to Very High Access)
Interactive Visualizations: Real-time map generation with color-coded predictions
Confidence Metrics: Statistical measures of prediction reliability and model performance
Regional Comparisons: Benchmarking against similar geographic areas and historical data

🎯 Key Advantages

🚀 Performance

Fast Processing: Optimized algorithms for real-time analysis with efficient data pipelines
Scalable Architecture: Handles datasets from small studies to national surveys with session management
Efficient Memory Usage: Streamlined data processing pipelines with user-specific storage
Interactive Visualizations: Plotly-powered maps with smooth zoom and hover functionality

👥 User Experience

Intuitive Interface: No technical expertise required with guided workflows
Responsive Design: Works seamlessly across all devices with adaptive layouts
Comprehensive Authentication: Secure login system with session-based data isolation
Real-time Feedback: Interactive flash messages and progress indicators

🔒 Reliability

Robust Error Handling: Graceful handling of edge cases with user-friendly error messages
Data Validation: Comprehensive input verification with intelligent column mapping
Model Persistence: Consistent predictions across sessions using trained XGBoost models
Session Security: Protected user data with secure password hashing and session management

🌐 Accessibility

Web-based Platform: No software installation required with modern browser support
Cross-platform Compatibility: Works on Windows, macOS, and Linux with responsive design
Multi-format Support: Flexible CSV import with intelligent column recognition
Interactive Features: Hover tooltips, zoom controls, and accessible navigation

📊 Use Cases

Healthcare Policy

Resource Allocation: Identify underserved regions requiring healthcare investment
Program Evaluation: Assess effectiveness of public health interventions
Trend Analysis: Monitor long-term health outcome improvements

Research Applications

Epidemiological Studies: Analyze disease patterns and correlations
Health Equity Research: Investigate healthcare disparities
Predictive Analytics: Forecast future healthcare needs

Clinical Decision Support

Population Health: Understand community health profiles
Risk Stratification: Identify high-risk patient populations
Intervention Planning: Target resources for maximum impact

🤝 Contributing

We welcome contributions to improve EquiHealth! Please follow these steps:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📝 License

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

📞 Support

For questions, suggestions, or support:

Email: [email protected]
Documentation: Project Wiki
Issues: GitHub Issues

🔮 Future Enhancements

Enhanced ML Models: Deep learning integration for complex health pattern recognition
Real-time Data Feeds: Live healthcare data integration from government APIs
Advanced Analytics Dashboard: Comprehensive KPI tracking and trend analysis
API Development: RESTful endpoints for external systems and third-party integrations
Multi-language Support: Internationalization for global healthcare applications
Mobile Application: Native iOS and Android apps with offline capability
Advanced Visualizations: 3D geographic rendering and time-series animations
Collaborative Features: Multi-user data sharing and team-based analysis tools

📅 Recent Updates (July 2025)

🎯 Major Enhancements Implemented:

✅ Interactive Map Visualization

Upgraded from static matplotlib to dynamic Plotly maps
Added hover functionality showing district details and predictions
Implemented zoom and pan capabilities for detailed exploration

✅ User Authentication & Session Management

Complete login/signup system with secure password hashing
Session-based data isolation for multi-user support
User-specific map generation and data storage

✅ Enhanced User Interface

Consistent navigation across all pages with context-aware links
Professional glassmorphism design with backdrop blur effects
Responsive layouts optimized for all device sizes

✅ Improved Data Pipeline

Intelligent CSV column mapping with flexible naming conventions
Real-time ML predictions using trained XGBoost models
Enhanced error handling with user-friendly feedback messages

✅ Technical Infrastructure

Plotly integration for interactive visualizations
User-specific file generation for maps and reports
Enhanced CSS framework with modern styling patterns

Built with ❤️ for better healthcare outcomes

Website • Docs • Requirements

Ready to deploy EquiHealth to production? Check out our comprehensive deployment guide:

📋 View Complete Deployment Guide

Quick Deploy Options:

🥇 Render (Recommended for beginners - Free tier with HTTPS)
🚀 Railway (Modern platform with excellent Python support)
🐍 PythonAnywhere (Python-focused hosting)
☁️ Heroku (Enterprise-grade platform)

The deployment guide includes:

Step-by-step instructions for each platform
Production configuration setup
Security considerations
Monitoring and analytics setup
Pre-deployment checklist

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
health-data		health-data
screenshots		screenshots
static		static
templates		templates
.gitignore		.gitignore
DEPLOYMENT.md		DEPLOYMENT.md
LICENSE		LICENSE
Procfile		Procfile
README.md		README.md
app.py		app.py
config.py		config.py
healthcare_access_xgboost_model.ipynb		healthcare_access_xgboost_model.ipynb
launch.json		launch.json
map.py		map.py
ranking-map.png		ranking-map.png
requirements.txt		requirements.txt
runtime.txt		runtime.txt
scripts.js		scripts.js
settings.json		settings.json

License

MaheshR03/Equihealth

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