Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, emphasizing the urgent need for accurate and early detection methods. This research aims to enhance the predictive performance of heart disease detection by leveraging two powerful machine learning techniques: Extreme Gradient Boosting (XGBoost) and Autoencoders. Using the publicly available Cardiovascular Disease Dataset, we develop and evaluate models based on both techniques, with a focus on key classification metrics—Accuracy, Precision, Recall (Sensitivity), Specificity, and F1 Score. The XGBoost model is trained
directly on the dataset, while the Autoencoder is employed for both anomaly detection and feature extraction. The study compares the performance of these models and explores the potential of a hybrid approach combining Autoencoder-based features with XGBoost classification. Results indicate that both models show promise, with XGBoost achieving high classification accuracy and Autoencoders contributing to enhanced feature representation. This work contributes to the growing field of AIassisted medical diagnostics and offers insights into model selection for heart disease prediction tasks.