Heart Disease Prediction

This project implements a Heart Disease Prediction model using machine learning algorithms to assist healthcare professionals in diagnosing the likelihood of heart disease in patients based on medical records and lifestyle data.

Table of Contents

• Installation
• Usage
• Evaluation
• Contributing

Installation

To run this project, you need to have the following Python packages installed:

• pandas
• numpy
• seaborn
• matplotlib
• scikit-learn

You can install these packages using pip:

pip install pandas numpy seaborn matplotlib scikit-learn

Usage

1. Load the dataset
   Load the dataset containing patient health data (age, cholesterol levels, blood pressure, etc.) using pandas.
2. Preprocess the data
   Handle missing values, perform feature scaling, and encode categorical variables if needed.
3. Train the model
   Train a machine learning model, to predict the likelihood of heart disease based on the preprocessed training data.
4. Evaluate the model
   Evaluate the model's performance on a test set using metrics like accuracy, precision, recall, and F1 score.

Evaluation

The performance of the model is evaluated using:

• Accuracy: The proportion of correct predictions.
• F1 Score: A metric that considers both precision and recall, especially useful for imbalanced datasets.
• Precision and Recall: Precision measures the accuracy of positive predictions, while recall measures the ability to find all relevant positive cases.

Contributing

Contributions are welcome! If you'd like to contribute, please:

1. Fork the repository.
2. Create a new branch.
3. Make your changes.
4. Open a pull request.