Inspiration
According to WHO, nearly 5.8 crore people die due to cardiac stroke every year. Among them, almost 80% of the patients die due to lack of proper first aid. The doctors to Advanced Cardiac Life Support (ACLS) claim that 50% of the patients can be saved if treated at proper time. So, I have come up with a solution in form of a web application which predicts whether any individual has any risk of cardiac arrest or not.
What it does
The web application is built using Streamlit, HTML, CSS which will predict the risk of cardiac arrest. The application uses Machine Learning Classification model at the back-end, which predicts the risk of cardiac arrest at an accuracy of 90%. The UI is user-friendly, so that each and every individual can take the benefit out of it.
How we built it
The application is built using a Python Library Streamlit, HTML, CSS and Machine Learning.
Challenges we ran into
Integrating the Machine Learning model with the web application is the major challenge. However, I have done it successfully.
Accomplishments that we're proud of
The application works!
What we learned
Integrating Machine Learning in web.
What's next for Cardiac Check
I will work on improving the accuracy of the model using hyper-parameter tuning and optimization.
Built With
- css
- html
- machine-learning
- python
- streamlit
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