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Heart Failure Prediction

This repository contains a complete machine learning project for predicting the likelihood of heart failure based on patient clinical data.

🖼️ Project Screenshot

Main Interface

📊 Dataset

We used the Heart Failure Clinical Records Dataset from the UCI Machine Learning Repository. It includes 13 clinical features such as age, serum creatinine, ejection fraction, and more, with a binary target indicating whether the patient experienced a heart failure event.

🛠️ Features

  • Data loading and preprocessing
  • Training and testing multiple classification models
  • Evaluation using accuracy and confusion matrix
  • User-friendly web interface with Gradio

🧠 Machine Learning Models Used

The following ML algorithms were implemented and compared:

  • Logistic Regression
  • Support Vector Machine (SVM)
  • K-Nearest Neighbors (KNN)
  • AdaBoost
  • LightGBM
  • Naive Bayes
  • Multi-Layer Perceptron (MLP)

🚀 How to Run the Project

  1. Clone the repository:

    git clone https://github.com/yousef-788/heart-failure-prediction.git
cd heart-failure-prediction

  2. Install the required packages:

    pip install -r requirements.txt

  3. Run the Gradio app:

    python app.py

This will open a local Gradio interface in your browser for real-time prediction.

📁 Project Structure

  • app.py – The main Python script running the ML model and Gradio interface
  • app.ipynb – Jupyter notebook used for development and exploration
  • heart_failure_clinical_records_dataset.csv – The dataset
  • requirements.txt – List of required Python libraries

🔍 Input Features

The app takes the following clinical inputs:

  • Age
  • Anaemia (0 or 1)
  • Creatinine Phosphokinase
  • Diabetes (0 or 1)
  • Ejection Fraction
  • High Blood Pressure (0 or 1)
  • Platelets
  • Serum Creatinine
  • Serum Sodium
  • Sex (0 = female, 1 = male)
  • Smoking (0 or 1)
  • Time (follow-up period in days)

✅ Output

The output is a binary prediction:

  • 1: High risk of heart failure
  • 0: Low risk of heart failure

🧰 Technologies Used

  • Python
  • Pandas, NumPy, Scikit-learn
  • Gradio (for UI)
  • Jupyter Notebook

💻 Author

Yousef Hamdy
GitHub
LinkedIn

📄 License

This project is licensed under the MIT License.

Feel free to fork this repository, use it, or improve it!