Heart Disease Prediction

This project aims to predict the presence of heart disease based on various medical attributes of an individual. It utilizes a logistic regression model trained on a dataset containing information about patients and whether they have heart disease or not.

Requirements

• Python 3.x
• NumPy
• Pandas
• scikit-learn

Installation

1. Clone the repository:

git clone https://github.com/real0x0a1/heart-disease-prediction.git

2. Install dependencies:

pip3 install -r requirements.txt

Usage

1. Ensure you have the dataset data.csv in the content directory.
2. Run the script heart_disease_prediction.py:

python3 heart_disease_prediction.py

3. The script will train a logistic regression model, evaluate its accuracy on training and test data, and make predictions for a sample input data.

Dataset

The dataset (data.csv) contains the following columns:

• age: Age of the patient
• sex: Gender (0: female, 1: male)
• cp: Chest pain type (0-3)
• trestbps: Resting blood pressure (mm Hg)
• chol: Serum cholesterol (mg/dl)
• fbs: Fasting blood sugar > 120 mg/dl (0: false, 1: true)
• restecg: Resting electrocardiographic results (0-2)
• thalach: Maximum heart rate achieved
• exang: Exercise-induced angina (0: no, 1: yes)
• oldpeak: ST depression induced by exercise relative to rest
• slope: Slope of the peak exercise ST segment (0-2)
• thal: Thalassemia (1-3)
• target: Presence of heart disease (0: no, 1: yes)

Acknowledgments

This project is inspired by the work on heart disease prediction and utilizes the dataset from the UCI Machine Learning Repository.

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Author

real0x0a1 (Ali)

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