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Project Info

PredictAPulseAI

Devpost

This project did not submit a demo video on Devpost.

Inspiration

The American Healthcare system is expensive and complicated. Everyone wants the best, most cost-effective insurance plan, but choosing one can feel like a daunting task. We were motivated to build a product that could deal with large benefits summaries containing opaque language and support our users' unique medical needs, all the while maintaining a high level of user personalization.

What it does

PredictAPulseAI provides users with a health questionnaire, where the answers serve as input for an ML model for heart attack risk classification. Our ML model was trained using a dataset that has features for causes of heart attacks and predicts future heart attacks. Afterward, users upload summary benefits of insurance policies to PredictAPulseAI, and it combines all of this data to find the most cost-effective insurance policy given your risk for heart attacks.

How we built it

Frontend: JS, React, Next.js, TypeScript, HTML/CSS, Material UI Backend: Python, Flask, MindsDB.api, Tesseract OCR, Open AI GPT (3.5) Database: SQL, CockroachDB Classification Model: Heart Attack Kaggle Dataset, MindsDB

Challenges we ran into

Some of the challenges we ran into were dealing with the limitations of Intel Cloud, specifically its inability to connect to Cockroach DB and port-forward for our custom backend API.

Accomplishments we're proud of

Successful implementations of functions. Replaced cookie usage. Bridged front-end to back-end, API integration, and database implementation.

What we learned

Insurance comparisons and how it helps users based on their current health conditions. We expanded our knowledge of SQL with CockroachDB, ML prediction models with MindsDB, and Flask web server with our custom-written API endpoint.

What's next

This project is not limited to predicting heart attacks and reducing the cost of treatment for our users. Other leading causes of death, such as cancer, can be effectively predicted to do the same pipeline. Most importantly, our project would save lives. We plan to adapt our project to an intuitive mobile application to increase accessibility. We also plan to effectively market our idea and utilize sponsorships from insurance companies to gain funding.

Analysis

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Metric

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • CSSIn code
  • FastAPIIn code
  • JavaScriptIn code
  • Next.jsIn code
  • OpenAIIn code
  • PythonIn code
  • ReactIn code
  • Tailwind CSSIn code
  • TypeScriptIn code
  • FlaskClaimed
  • SQLClaimed
  • VercelClaimed

9 of 12 appear in the indexed code. 3 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

31 KB

Source files

11

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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