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Stroke Prediction App

This GitHub repository contains the code for a Stroke Prediction App. The app is built using Streamlit, and it predicts the likelihood of a stroke based on real-life data. The model used for predictions is trained on a dataset of healthcare records.

Features

  • Predicts the likelihood of a stroke based on input features such as age, gender, hypertension, heart disease, ever married, work type, residence type, average glucose level, BMI, and smoking status.
  • The app uses a machine learning model (XGBoost) trained on a dataset of healthcare records for predictions.
  • The app can be hosted and used on Streamlit.

Usage

To run the app locally, follow these steps:

  1. Clone the repository to your local machine.
  2. Make sure you have the required Python packages installed: streamlit, pandas, scikit-learn, xgboost, and numpy.
  3. Run the following command in your terminal: streamlit run app.py
  4. Open the URL displayed in your terminal (usually http://localhost:8501) to access the app.

Data

The data used for training the model is provided in the healthcare-dataset-stroke-data.csv file. It contains information about patients, including their age, gender, hypertension, heart disease, work type, residence type, average glucose level, BMI, and smoking status, as well as whether they had a stroke.

Model

The XGBoost classifier model is trained on the healthcare dataset to predict the likelihood of a stroke. The trained model is saved as model.json and is used in the app to make predictions.

Dependencies

  • streamlit
  • pandas
  • scikit-learn
  • xgboost
  • numpy