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

📊 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

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📄 License

This project is licensed under the MIT License.

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