This repository contains code for a web application designed to predict the likelihood of a person experiencing a stroke based on various demographic and health-related features. The application is built using Python and FastAPI for the backend, and Streamlit.
web.app.mp4
The web application utilizes a machine learning model trained on a dataset containing information about individuals' gender, age, hypertension, heart disease, marital status, work type, residence type, average glucose level, body mass index (BMI), and smoking status. The features used for prediction include:
- Gender: The gender of the individual.
- Age: The age of the individual.
- Hypertension: Whether the individual has hypertension (1 for yes, 0 for no).
- Heart Disease: Whether the individual has heart disease (1 for yes, 0 for no).
- Ever Married: Whether the individual is ever married (1 for yes, 0 for no).
- Work Type: The type of work the individual is engaged in (e.g., private, self-employed, etc.).
- Residence Type: The type of residence of the individual (urban or rural).
- Average Glucose Level: The average glucose level of the individual.
- BMI: The body mass index (BMI) of the individual.
To use the web application, follow these steps:
- Visit the web application at Cerebral Stroke Prediction Web App.
- Input the required information for prediction.
- Click the submit button to get the prediction result.