PPCS

Our project diagnoses users who potentially could have heart disease, diabetes, and more through a machine learning model made with python that uses data from hundreds of pre diagnosed patients.

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Inspiration We wanted to create a tool that helps people assess their risk of heart disease and chronic kidney disease based on their health information. We mean for the data points which people put in as inputs to be data which users can get at home.

What it does The app takes user input (age, blood pressure, diabetes status), predicts the risk of heart disease and kidney disease using machine learning models, and shows the results. It also generates an email which can be sent to doctors.

How we built it We manipulated code generated by Chat GPT to fit our goals and visions for this project.

Accomplishments that we're proud of Functional App: Successfully built a working tool that integrates machine learning with a user-friendly interface. Visual Indicators: Implemented risk bars to visually represent the assessment results. Email Feature: Added a feature to generate and display a detailed summary email. What we learned Model Integration: How to incorporate machine learning models into a desktop application. UI/UX Design: Techniques for designing a user-friendly interface. Data Limitations: The challenges of using synthetic data for model training.

What's next for PPCS Refining the UI: Cleaner interface to use, that looks more modern. Expanding the Disease Risk Evaluation: Adding other conditi

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