MedX has the prime objective of a disease prediction system, along with a secure payment system using Solana blockchain and a smart contract for booking an appointment with the doctor.
We were looking at data on the level of healthcare people all around the world were receiving and one of the things we found was that people in rural areas are clueless about the symptoms of the disease and also lack proper medical supervision/care.
Hence, Team 1 came up with the idea of providing people a web application that will not only connect those in need with the correct medical care/supervision but also enable them to understand the implications of different diseases in a very intuitive way.
MedX is a one-stop location to discuss different medical milestones, and AI Disease prediction systems, transfer money through a secure payment system using Solana blockchain, and finally, a translator to get the whole web app in the language one is comfortable with and hence dissolve any communication barrier. This solution will shorten the gap between the people around the world in search of good healthcare and pave a path in the journey of recovery.
- React Js: For frontend
- Firebase: For backend
- Solana: For secure payment
- Tensorflow.s: For ML and disease prediction
- Google Teachable Machine: For training the model
- i18n: For multi-language support
- Tailwind: For styling and animations
We are using Solana for building a secure decentralized and secure payment platform. It is a decentralized, distributed, and open-source blockchain that is designed to be a secure, scalable, and reliable platform for smart contracts.
We used GitHub for version control and collaboration. We used GitHub Project to plan our project. We used GitHub Issues to track bugs and issues. We used GitHub Pull Requests to track feature requests. We also used GitHub Actions to automate our CI/CD pipeline. GitHub Actions is a powerful tool that allows you to automate your CI/CD pipeline.
- As some of the teammates were from different time zones, it was a bit difficult to collaborate, but we managed to get the project done.
- Some of us did not have any experience with Machine Learning. We collaborated and helped each other get up to speed. We managed to get most features we want working.
- Completing the project within the given time frame.
- Creating a fully functional application.
- How to use Google teachable machine to predict the disease.
- How to use i18n to support multi-language support.
- How to use Tailwind to style and animate our application.
- Building a mobile application.
- Adding more disease prediction features.
- Improving the accuracy of our ML model.