Personalized Healthcare Recommendation System

The Personalized Healthcare Recommendation System is an AI-powered application designed to provide tailored healthcare advice based on a user’s medical history, lifestyle data, and health goals. The platform integrates machine learning to assess health risks and offers actionable recommendations for preventive care and chronic condition management.
SECTOR
Healthcare
PROJECT TYPE
Full Stack
Technologies
ReactJS
Python (Flask)
TensorFlow
AWS Lambda
Redis Cache
PostgreSQL
Kubernetes (AWS EKS)
Docker
AWS RDS

Goal of the project

The project aimed to:

  • Build a personalized healthcare system that analyzes user health data and provides customized recommendations.
  • Predict potential health risks using machine learning models.
  • Offer a user-friendly interface to track health goals, reminders for medications, and appointment scheduling.
  • Ensure secure handling of sensitive health data in compliance with privacy standards like HIPAA.

Project Execution

UI/UX Design

I utilized a free design from the Figma Community, created by Peace Ojo.

Architecture

The system was built using a modular architecture to ensure scalability and flexibility:

Architecture Diagram
  1. Frontend:
    • Designed with React.js for a responsive and interactive user experience.
    • Features include a user profile dashboard, health tracking graphs, and personalized recommendation panels.
  2. Backend:
    • Developed with Python (Flask) to manage data processing and API endpoints. The backend handles business logic, user authentication, health data processing, and communication with external APIs (Google Fit).
    • Implemented JWT-based authentication for secure user sessions.
  3. Machine Learning:
    • Built predictive models using Scikit-Learn and TensorFlow to identify health risks and recommend preventive measures.
    • Models were trained on public health datasets to ensure robust predictions.
  4. Data Integration:
    • Connected with wearable device APIs (Google Fit) to fetch real-time health data such as steps, heart rate, and sleep patterns.
    • Supported manual data entry for users without connected devices.
  5. Notifications:
    • Integrated AWS Simple Notification Service (SNS) to send reminders for medications, appointments, and health checkups.
  6. Database:
    • PostgreSQL for storing user profiles, health records, and recommendations.
    • Redis for caching frequently accessed data to improve response times.
  7. Deployment:
    • Used Docker for containerizing the application and Kubernetes (AWS EKS) for orchestrating scalable deployment.

Tech Stack

  • Frontend: React.js, Material-UI
  • Backend: Python (Flask), REST APIs
  • Machine Learning: Scikit-Learn, TensorFlow
  • Database: PostgreSQL, Redis (caching)
  • Integration: Fitbit/Google Fit APIs
  • Cloud Services: AWS S3, AWS EKS, AWS SNS
  • Tools Used:
    • Postman for API testing.
    • Docker for containerization.
    • Helm for Kubernetes configuration management.

Timelines

The project was executed over 12 weeks:

  • Week 1-3: Requirement gathering, dataset preparation, and architecture design.
  • Week 4-6: Backend development and API integration.
  • Week 7-8: Machine learning model development and training.
  • Week 9-10: Frontend development and integration with backend services.
  • Week 11: Deployment on AWS EKS.
  • Week 12: Testing, performance tuning, and user feedback integration.