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Heart Failure Predictor ❤️‍🩹

A machine learning model built with Logistic Regression to predict the likelihood of heart failure based on clinical data.

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🚀 Features

• Accurate Predictions: Leverages Logistic Regression for reliable predictions.
• Interactive Interface: Accepts user inputs for clinical parameters and provides predictions.
• Kaggle Dataset: Trained on real-world heart failure data.
• Customizable: Easily adaptable for additional features or datasets.

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🛠️ Technologies Used

• Python: Programming language.
• Logistic Regression: For prediction modeling.
• Pandas: Data manipulation.
• NumPy: Numerical computations.
• Scikit-learn: Machine learning library.
• Flask/Streamlit: (Specify) Used for deploying the model as a web application.

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📊 Dataset

• Source: Kaggle Heart Failure Dataset
• Features Used:
  • Age
  • Anaemia
  • Creatinine Phosphokinase
  • Diabetes
  • Ejection Fraction
  • High Blood Pressure
  • Platelets
  • Serum Creatinine
  • Serum Sodium
  • Sex
  • Smoking
  • Time (Follow-up period)

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📂 Project Structure

HeartFailurePredictor/
├── data/                  # Dataset files
├── model/                 # Trained model files
├── app/                   # Application files (Flask/Streamlit)
├── notebooks/             # Jupyter notebooks for EDA and training
├── requirements.txt       # Dependencies
└── README.md              # Project documentation

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📦 Installation and Setup

Prerequisites

1. Install Python (v3.8 or later).

Steps

1. Clone the repository:

   git clone https://github.com/Abhii039/Heart-Failure-Predictor-Model.git
   cd Heart-Failure-Predictor-Model

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   python app.py

4. Open your browser and navigate to http://localhost:5000 (or Streamlit URL).

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📖 Model Performance

• Accuracy: 85%
• Precision: 83%
• Recall: 80%
• AUC-ROC Score: 0.87

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🌐 Live Demo

Check out the live application here: Heart Failure Predictor Demo

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🚧 Roadmap

• Add support for other machine learning models.
• Enhance the user interface.
• Deploy to cloud platforms like AWS or Heroku.
• Integrate a larger dataset for improved accuracy.

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🛡️ License

This project is licensed under the MIT License.

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🙌 Contributing

1. Fork the repository.
2. Create a feature branch (git checkout -b feature-name).
3. Commit your changes (git commit -m "Add new feature").
4. Push to the branch (git push origin feature-name).
5. Open a Pull Request.

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📧 Contact

For any inquiries or suggestions, please contact:

• Name: Abhi Dobariya
• Email: [email protected]
• GitHub: Your GitHub Profile

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Feel free to customize this as per your project! 😊