Inspired by the prospects of trying to understand a huge reason why women are underrepresented in the STEM field, we sought to tackle a question of predicting menopause age to ensure them that their "biological clocks" were not ticking as fast as they might presume. Thus, there is most definitely enough time for them to both undertake an exciting career in STEM and raise a great family-the two are not mutually exclusive!

What it does

This app uses known risk factors to predict the age of menopause in women. The risk prediction model, which has never yet been developed in the general population, uses a machine learning algorithm on a nationally representative database from the Center for Disease Control (CDC) to generate a predicted age of menopause based on a variety of significant predictors. After entering personalized information, more personalized information is provided to the user about their predicted age of menopause relative to the general population. As an added exciting function to incorporate men using this app, there is also a feature to estimate age of erectile dysfunction.

How we built it

This application was built on a R Shiny platform.

Challenges we ran into

A challenge we faced was coming up with an application that would be both novel, and useful for our target population. We also faced challenges in finding the best way for us all to be simultaneously contributing to the coding aspect of our project.

Accomplishments that we're proud of

Prior to this Hackathon, we had never worked with R shiny, so we are proud to have a clean finished project that demonstrates our ability to code, as well as our capabilities in creating linear models.

What we learned

We learned the importance of communication and patience while coding. We also learned of the excitement felt when an app runs as expected.

What's next for Predicting the Age of Menopause

We would like to turn our ED portion of the app into a linear prediction model. We would also like to refine our risk factors for menopause to include more significant information such as maternal family history.

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