Inspiration
The idea of the Blood Match Prediction System was born from a thought to use technology for social impact. Learning about the struggles Thalassemia patients face, especially the urgency of blood transfusions. This led to a question for me: What if we could predict and prevent the crisis before it happens? So I wanted to build something that didn't just react to emergencies, but also save lives on time.
What it does
It uses ML to forecast when a donor is likely to donate again based on past behaviour. Match patients with nearby compatible donors using type, urgency, and availability. Track contributions and reward consistent donors with badges and social impact stats.
How we built it
The tech stack that can be useful to build this project is: Frontend: HTML, CSS, and React.js Backend: Python integrated with a machine learning model trained on donor behaviour and availability patterns. Database: MongoDB or PostgreSQL Integration: REST APIs for e-RaktKosh and Blood Bridge Notifications: Firebase Cloud Messaging
Challenges we ran into
As per my knowledge, the biggest challenges for this project are: Data Scarcity, Balancing urgency with privacy, model accuracy, and Scalability
Accomplishments that we're proud of
What we learned
This project can learnt me many things. Especially how to blend empathy with engineering. How to apply machine learning to real-world problems. The importance of user-centric design.
What's next for Blood Match Prediction System
The extension for this project might be a partnership with hospitals and blood banks, mobile app development, advanced AI models, and open-source collaboration.
Built With
- cloud-messaging
- css
- html
- machine-learning
- mongodb
- postgresql
- python
- react.js
