This project aims to predict the likelihood of heart disease in individuals based on various health metrics and attributes. It utilizes machine learning algorithms to analyze a dataset containing features like age, sex, cholesterol levels, blood pressure, etc., to make predictions about the presence of heart disease.
The dataset used in this project is sourced from [Dataset Source]. It contains [number] instances with [number] features including:
- Age
- Sex
- Chest pain type
- Resting blood pressure
- Serum cholesterol
- Fasting blood sugar
- Resting electrocardiographic results
- Maximum heart rate achieved
- Exercise-induced angina
- ST depression induced by exercise relative to rest
- Slope of the peak exercise ST segment
- Number of major vessels colored by fluoroscopy
- Thallium stress test result
- Target: Presence of heart disease (0 for no, 1 for yes)
- Python 3.x
- Jupyter Notebook
- Libraries:
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn
- Clone this repository to your local machine.
- Install the required libraries using pip:
- Navigate to the directory where the project is cloned.
- Launch Jupyter Notebook:
- Open and run the
heart_disease_prediction.ipynbnotebook. - Follow the instructions within the notebook to train the model and make predictions.
The performance of the machine learning model is evaluated using metrics such as accuracy, precision, recall, and F1-score. Additionally, visualizations such as confusion matrices and ROC curves are used to assess the model's performance.
- Collect more diverse and comprehensive data to improve model accuracy.
- Experiment with different machine learning algorithms and hyperparameters to enhance predictive performance.
- Implement feature engineering techniques to extract more meaningful information from the data.
- Explore advanced techniques such as ensemble learning and neural networks for better predictions.