PHAS - Personal Health Analytics System

PHAS is a comprehensive health analytics platform that provides personalized health insights, risk predictions, and real-time monitoring for patients, healthcare providers, and administrators.

Features

• Role-based Access Control: Different dashboards for patients, healthcare providers, and administrators
• Health Risk Prediction: ML-powered prediction of chronic disease risk
• IoT Device Integration: Real-time monitoring of health metrics
• Personalized Recommendations: Health advice based on user data
• Notification System: Alerts for critical health readings

System Architecture

The application consists of:

1. Frontend: React/Next.js application with Tailwind CSS
2. Backend: Python Flask API server
3. Machine Learning: Predictive model for health risk assessment

Setup Instructions

Prerequisites

• Node.js (v14+)
• Python (v3.8+)
• pip (Python package manager)

Step 1: Install Frontend Dependencies

# Navigate to project root
cd /path/to/project

# Install Node.js dependencies
npm install

Step 2: Install Backend Dependencies

# Install Python dependencies
pip install flask flask-cors pandas scikit-learn joblib numpy

Step 3: Train the ML Model (Optional)

# Run the model training script
python "Model training.py"

Step 4: Start the Backend Server

# Start the Flask server
python backend_server.py

Step 5: Start the Frontend Development Server

# In a new terminal, start the Next.js development server
npm run dev

Step 6: Access the Application

Open your browser and navigate to:

http://localhost:3000

Demo Accounts

Use these credentials to test the application:

• Admin:

  • Email: admin@phas.com
  • Password: admin123
  • Role: admin

• Healthcare Provider:

  • Email: doctor@phas.com
  • Password: doctor123
  • Role: healthcare

• Patient:

  • Email: patient@phas.com
  • Password: patient123
  • Role: patient

Development

Project Structure

• /src/app: Next.js application pages
• /src/app/api: API routes for frontend-backend communication
• /src/components: React components
• /src/utilities: Utility functions
• backend_server.py: Flask API server
• Model training.py: ML model training script
• predictive_model.pkl: Serialized ML model

Future Enhancements

• Database integration for persistent storage
• User authentication with JWT
• FHIR standard compliance for healthcare data
• Mobile application support
• Enhanced visualization of health trendsThis project was generated from create.xyz.

It is a Next.js project built on React and TailwindCSS.

Getting Started

First, run the development server:

npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Open http://localhost:3000 with your browser to see the result.

You can start editing the code in src. The page auto-updates as you edit the file.

To learn more, take a look at the following resources:

• React Documentation - learn about React
• TailwindCSS Documentation - learn about TailwindCSS
• Next.js Documentation - learn about Next.js features and API.
• Learn Next.js - an interactive Next.js tutorial.