Inspiration
NYU social determinants of health research paper.
What it does
This application trains models using three different datasets of patients with and without heart disease. Taking in various factors about a patient that a doctor or the patient themselves may know, HDRC can immediately display the probability that this person develops heart disease. It can further recommend next steps that a patient can take depending on the data they've provided
How we built it
Challenges we ran into
Finding appropriate datasets for the various models we wanted to incorporate was difficult.
Accomplishments that we're proud of
Most predictive models in health has a unidimensional view of calculation, taking in biological factors like blood pressure, age, cholesterol. However, we view this issue from another angle, incorporating Social Determinant of Health into our probability model. This includes stats like Food Environment Index of the area, Median Income, Access to Education, etc. Furthermore, we take into account daily behaviors that might help us better predict heart failure, such as smoking, drinking, exercise, and so on.
What we learned
What's next for Heart Disease Risk Calculator
Built With
- flask
- javascript
- nextui
- node.js
- python
- react
- restapi
- scikit-learn
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