QuickHealth AI is an intuitive web application designed to empower individuals to take control of their health. The project features a Symptom Checker, which uses AI to provide potential health conditions based on user-reported symptoms, and a Health Tracker, where users can log daily metrics like weight, exercise minutes, and water intake. The idea is to make health monitoring accessible and engaging for everyone, encouraging proactive health management.
Inspiration The inspiration for QuickHealth AI came from the need for easy and reliable access to health insights. We realized that many people often face difficulties understanding their health conditions or keeping track of daily health metrics. With the advancements in artificial intelligence and web development, we wanted to create a simple yet effective tool that could help users gain valuable health insights and maintain healthy habits. What We Learned
Throughout the project, we learned:
Integrating Frontend and Backend: We gained hands-on experience in connecting a React frontend with a Node.js backend, learning the importance of efficient API communication and data handling.
Working with AI Models: Incorporating a pre-trained AI model and exposing it via a Flask API taught us how to bridge the gap between data science and web development.
Data Management with MongoDB: Using MongoDB for storing and retrieving user data helped us understand database design and the importance of efficient data querying.
Handling CORS and Cross-Origin Requests: We learned how to handle cross-origin issues, a common challenge when connecting separate frontend and backend servers.
Building Responsive User Interfaces: Working with React gave us valuable insights into designing user-friendly and responsive web interfaces that improve user engagement.
How We Built It
Frontend: We built the user interface using ReactJS, focusing on simplicity and user experience. We used axios for making API calls and integrated libraries like Chart.js to visualize health metrics.
Backend: The backend was developed using Node.js and Express.js. We implemented RESTful API endpoints for managing user data, health metrics, and connecting to the AI model.
Database: We used MongoDB to store user information and health metrics, using Mongoose for data modeling and seamless integration with our backend.
AI Model: The symptom checker uses a lightweight, pre-trained AI model written in Python and hosted via Flask. We exposed the model as a RESTful API and integrated it with the Node.js backend.
Deployment: We deployed the frontend and backend on platforms like Heroku and linked them to a custom domain for easy access.
Challenges We Faced
Cross-Origin Resource Sharing (CORS): One of the major hurdles was handling CORS issues when connecting the frontend and backend. Configuring CORS and ensuring secure communication between the servers took some time to resolve.
Data Synchronization: Ensuring that health metrics were logged correctly and retrieved in real-time required careful attention to API design and data management in MongoDB.
AI Model Integration: Exposing the AI model as an API and testing the model's performance under different scenarios was challenging. We had to ensure that the AI model returned accurate and timely predictions.
Time Management: Given our limited time frame, balancing the development of both the frontend and backend while maintaining code quality was a constant challenge.
Optimizing Performance: Ensuring that the app loaded quickly and handled user input efficiently, especially when making calls to the AI model, required performance optimizations.
What’s Next?
We envision expanding QuickHealth AI by adding features like:
User Authentication: To allow users to create accounts and securely store their health data.
More Advanced AI Models: Integrating more sophisticated models for better health insights and predictions.
Notifications and Reminders: To remind users to log their daily health metrics and stay consistent.
Mobile App: Developing a mobile version of QuickHealth AI for easier access and engagement.
We hope QuickHealth AI makes health monitoring simpler and more insightful for everyone, and we're excited to continue improving and expanding this project in the future!
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