This project aims to predict the presence of heart disease in patients using machine learning algorithms. The model is built using a dataset containing various health metrics and indicators, which are processed and analyzed to determine the likelihood of heart disease. The goal is to provide a reliable tool that can assist healthcare professionals in diagnosing heart disease early and accurately.
- Data Preprocessing: Cleaning and preparing the dataset for analysis.
- Exploratory Data Analysis (EDA): Visualizing and understanding the data distribution and relationships between features.
- Feature Engineering: Selecting and creating relevant features to improve model performance.
- Model Training: Implementing and training multiple machine learning algorithms.
- Model Evaluation: Assessing model performance using various metrics and selecting the best-performing model.
- Prediction: Using the trained model to predict heart disease on new data.
The dataset used in this project is the Heart Disease UCI dataset, which contains 303 instances and 14 attributes, including age, sex, chest pain type, resting blood pressure, cholesterol levels, fasting blood sugar, rest ECG results, maximum heart rate achieved, exercise-induced angina, ST depression induced by exercise, slope of the peak exercise ST segment, number of major vessels colored by fluoroscopy, and thalassemia.
- Python 3.7+
- pandas
- numpy
- scikit-learn
- matplotlib
- seaborn
- jupyter
-
Clone the repository:
git clone https://github.com/yourusername/heart-disease-prediction.git cd heart-disease-prediction -
Create and activate a virtual environment:
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
-
Install the required packages:
pip install -r requirements.txt
-
Start Jupyter Notebook:
jupyter notebook
Open the heart_disease_prediction.ipynb notebook in Jupyter and follow the steps to preprocess the data, perform EDA, train the models, and make predictions. Each section of the notebook contains detailed instructions and explanations.
The project evaluates several machine learning models, including Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines. The models are compared based on accuracy, precision, recall, and F1-score. The best-performing model is saved and can be used to make predictions on new data.
Contributions are welcome! Please fork the repository and create a pull request with your changes. Ensure that your code adheres to the project's coding standards and includes appropriate tests.
This project is based on the Heart Disease UCI dataset available at the UCI Machine Learning Repository. Special thanks to the contributors of this dataset and the open-source community for providing tools and resources that made this project possible.