Cardiovascular Disease Prediction Using Machine Learning: An XGBoost Approach with Hyperparameter Tuning

Abstract Cardiovascular diseases (CVDs) are one of the leading causes of mortality worldwide. Early diagnosis and intervention are crucial to reducing the risk associated with these diseases. In this study, we propose a machine learning-based system for predicting cardiovascular disease using the Extreme Gradient Boosting (XGBoost) technique. We employ feature selection and hyperparameter optimization using random search to enhance the model’s accuracy. The model’s performance is evaluated through cross-validation and compared with other algorithms, including K-Nearest Neighbors (KNN), Na¨ıve Bayes, Support Vector Machine (SVM), and Random Forest. Experimental results show that our XGBoost-based model outperforms the other algorithms with an accuracy of 98% and an area under the ROC curve of 0.98.