The Heart Disease Prediction Project is a machine learning-based application that predicts the likelihood of heart disease in individuals based on key medical parameters. It provides a user-friendly interface for inputting health data and delivers accurate, real-time predictions to assist in early diagnosis and prevention.
- Launch the application.
- Input the required health parameters such as age, blood pressure, cholesterol levels, etc.
- Click the "Predict" button to receive:
- Heart disease likelihood score
- Risk level classification (e.g., Low, Medium, High)
- Heart Disease Prediction: Predicts the probability of heart disease based on medical data.
- User-Friendly Interface: Interactive and easy-to-use web application.
- Visual Insights: Displays risk levels in a clear and understandable format.
- Accurate Predictions: Achieved using advanced machine learning algorithms.
- Python: Core programming language for model development.
- Flask: Framework for building the web application.
- Pandas & Numpy: For data preprocessing and analysis.
- Scikit-learn: For implementing and training machine learning models.
- Matplotlib & Seaborn: For visualizing health data and prediction results.
The dataset includes medical parameters relevant to heart disease diagnosis, such as:
- Age
- Sex
- Chest pain type
- Resting blood pressure
- Serum cholesterol levels
- Fasting blood sugar
- Maximum heart rate achieved
- Exercise-induced angina
- Other diagnostic attributes
- Real-Time Health Monitoring: Integrate wearable devices for continuous data input.
- Personalized Reports: Provide detailed health reports for users.
- Mobile App Version: Develop a mobile application for better accessibility.
- Multi-Language Support: Add language options for a global user base.
For more details and the source code, visit the project repository.
Stay Heart Healthy! ❤️