Heart Failure Prediction

This repository focuses on predicting heart failure based on the Hungarian dataset, which can be found here. The dataset contains 76 attributes, but this experiment concentrates on 14 key attributes: age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal, and target.

Dataset Information

• age: Age of the patient
• sex: Gender of the patient (1 = male, 0 = female)
• cp: Chest pain type (Value 1-4)
• trestbps: Resting blood pressure (mm Hg)
• chol: Serum cholesterol in mg/dl
• fbs: Fasting blood sugar > 120 mg/dl (1 = true; 0 = false)
• restecg: Resting electrocardiographic results (values 0,1,2)
• thalach: Maximum heart rate achieved
• exang: Exercise induced angina (1 = yes; 0 = no)
• oldpeak: ST depression induced by exercise relative to rest
• slope: The slope of the peak exercise ST segment
• ca: Number of major vessels (0-3) colored by fluoroscopy
• thal: Thalassemia (3 = normal; 6 = fixed defect; 7 = reversible defect)
• target: Presence of heart disease (1 = yes; 0 = no)

Screenshot

Deployment

The prediction model is deployed using Streamlit, and you can interact with it here.

Usage

To replicate the experiment or use the prediction model locally:

1. Clone this repository.

   git clone https://github.com/bimarakajati/Heart-Failure-Prediction.git
   cd heart-failure-prediction

2. Install the required packages.

   pip install -r requirements.txt

3. Run the Streamlit app.

   streamlit run app.py

Now, you can access the prediction model locally through your web browser.

Feel free to explore and contribute to this project!