Heart Disease Prediction is a Streamlit web application developed to predict the likelihood of a user experiencing heart failure based on various input parameters. The app takes inputs such as cholesterol level, resting blood pressure, ST slope, chest pain type, age, gender, ECG results, and other parameters to predict the outcome.
- You can view my analysis and model creation in jupyter notebook model.ipynb✌️
- Predictive Modeling: Utilizes machine learning algorithms like Random Forest, XGBoost to accurately predict the likelihood of heart disease based on user input.
- User-friendly Interface: Offers an intuitive and interactive interface for users to input their data and view the prediction results.
- Input Parameters: Allows users to input various parameters such as cholesterol level, blood pressure, age, etc., for personalized predictions.
- Real-time Prediction: Provides instant prediction results based on the user's input data.
Click to Launch the Streamlit Web App
The Heart Disease Prediction web app is designed to assist users in assessing their risk of heart disease based on their individual characteristics and medical history. By leveraging machine learning techniques, the app offers personalized predictions, empowering users to make informed decisions about their cardiovascular health.
Feel free to explore the app and provide feedback or suggestions for improvement! 📩