Heart Disease Prediction

This project implements a machine learning model to predict the presence of heart disease based on various health-related features. The model uses Logistic Regression to classify whether a person is at risk of heart disease or not.

Dataset

The dataset used for training the model is the heart_disease_data.csv file. It contains health-related features such as age, sex, cholesterol levels, resting blood pressure, and more. The target variable is target, where:

• 1 indicates the person has heart disease
• 0 indicates the person does not have heart disease

Data Features:

• Age: Age of the person
• Sex: Gender (0 = female, 1 = male)
• Chest pain type: Type of chest pain experienced
• Resting blood pressure: Blood pressure when resting
• Serum cholesterol: Cholesterol levels in mg/dl
• Fasting blood sugar: Whether the person has fasting blood sugar greater than 120 mg/dl
• Resting electrocardiographic results: Results of the electrocardiogram
• Maximum heart rate achieved: Highest heart rate achieved
• Exercise induced angina: Whether the person has induced angina during exercise
• ST depression induced by exercise: The depression of ST segment during exercise
• Slope of peak exercise ST segment: The slope of the ST segment during peak exercise
• Number of major vessels colored by fluoroscopy: Number of vessels colored
• Thalassemia: A blood disorder
• Target: Presence of heart disease (1 = disease, 0 = no disease)

Files in the Repository

• Heart_Disease_Detection.ipynb: Jupyter notebook containing the machine learning model, data exploration, and prediction process.
• heart_disease_data.csv: Dataset containing health-related information.

How to Run the Code

1. Clone this repository to your local machine using the following command:

   git clone https://github.com/Krisha-Bhalala/Heart-Disease-Prediction.git

2. Navigate to the project directory:

   cd Heart-Disease-Prediction

3. Install the required dependencies. If you have requirements.txt, you can use:

   pip install -r requirements.txt

   Alternatively, you can manually install the required libraries:

   pip install numpy pandas scikit-learn

4. Run the Jupyter Notebook:

   • Start Jupyter Notebook by running the following command:

   jupyter notebook

   • Open the Heart_Disease_Detection.ipynb notebook and execute the cells.

5. Make Predictions:

   • The notebook contains a section where you can input health data and get a prediction of whether the person is at risk for heart disease.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• The dataset is based on the Cleveland Heart Disease dataset from the UCI Machine Learning Repository.
• This project is part of my learning in machine learning and data science.