Cardiovascular diseases (CVDs) are a major global health concern, responsible for a significant number of deaths each year. Early detection and management of cardiovascular diseases are crucial to reduce the risk of heart attacks, strokes, and premature deaths. Machine learning models can play a vital role in predicting possible heart disease and assisting in early intervention for individuals at high cardiovascular risk.
This project uses a Random Forest Classifier to predict the likelihood of heart disease based on various features such as age, gender, chest pain type, resting blood pressure, cholesterol level, fasting blood sugar, resting electrocardiographic results, maximum heart rate, exercise-induced angina, oldpeak (ST depression induced by exercise relative to rest), and ST slope.
This machine learning algorithm is used in order to handle both numeric and categorical features, making it suitable for this classification task with a mix of feature types.
The following Python libraries are used:
- pandas
- numpy
- scikit-learn
The sample dataset used in this project is from Heart Failure Prediction Dataset.