<![CDATA[Cardiovascular diseases are conditions that commonly affect the cardiovascular system, such as heart disease and stroke. According to data from the World Health Organization (WHO), 17.9 million deaths worldwide in 2019 were attributable to cardiovascular disease. Early detection is crucial, but diagnosing heart disease is complex in developing countries due to the limited availability of diagnostic tools and medical personnel. This study uses the Heart Disease Dataset from Kaggle, consisting of 15 attributes and 4238 records, to develop a heart disease classification model using XGBoost. The research stages include data imputation, data transformation using LabelEncoder, data balancing using ADASYN, data splitting (80% training data, 20% testing data), and hyperparameter tuning with Bayesian Optimization. The results show that the XGBoost model with ADASYN performs better, with a ROC-AUC of 0.971 and an accuracy of 0.916, compared to the model without ADASYN, which has a ROC-AUC of 0.698 and an accuracy of 0.841. Based on the research results, ADASYN has proven effective in improving model performance on imbalanced datasets. Additionally, Bayesian Optimization plays an important role in finding the optimal parameter combination, which can further enhance model performance. With this research, the impact is quite significant in the development of early detection methods for cardiovascular heart disease, particularly through the application of the XGBoost classification algorithm]]>
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