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.
- 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)
The prediction model is deployed using Streamlit, and you can interact with it here.
To replicate the experiment or use the prediction model locally:
-
Clone this repository.
git clone https://github.com/bimarakajati/Heart-Failure-Prediction.git cd heart-failure-prediction -
Install the required packages.
pip install -r requirements.txt
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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!