https://github.com/01110011011101010110010001101111/inflatracker
An application to identify which unique ingredients cause Crohn Disease patients to have inflammation. Winner of the 2019 Congressional App Challenge under Joaquin Castro.
https://github.com/01110011011101010110010001101111/inflatracker
Last synced: about 2 months ago
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An application to identify which unique ingredients cause Crohn Disease patients to have inflammation. Winner of the 2019 Congressional App Challenge under Joaquin Castro.
- Host: GitHub
- URL: https://github.com/01110011011101010110010001101111/inflatracker
- Owner: 01110011011101010110010001101111
- License: mit
- Created: 2020-05-19T19:35:48.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-06-15T23:07:08.000Z (almost 5 years ago)
- Last Synced: 2025-01-31T11:50:12.926Z (4 months ago)
- Language: Python
- Homepage:
- Size: 10.2 MB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# InflaTracker
An application to identify which unique ingredients cause Crohn Disease patients to have inflammation
### Inspiration
One of our teammate is suffering from Crohn's Disease. For a Crohn's Disease patient, it is often difficult to identify which ingredients in their foods causes inflammation of their stomach.
### What it Does
Our app allows users to track foods, writing whether they cause or did not cause inflammation. Using Machine Learning, our algorithm looks at the ingredients of all the foods the user has eaten and identifies which ingredients cause the inflammation and which do not.
### Version 1.0
In our first version, submitted to and winning the Congressional App Challenge under Joaquin Castro, the application was able to create user accounts and make predictions of which ingredients and foods are safe and which could cause inflammation. We had three screen: a profile page with the recommendations and options to change accounts, a statistics page with a graph showing the foods and which are safe and which are not, and a search bar to find foods. We built the application using HTML and Flask, and we used K-Means to conduct the analysis.
### Future Expansion for Version 2.0
For our next version, we hope to add a database, increase the speed of the application, add a more robust algorithm, and tidy the UI.
### Contact Us
If you have any questions about the application or if you'd like to help, please contact us at [email protected]. We'd love to hear from you!