<![CDATA[mortality rates. This study aims to analyze and compare the performance of two machine learning algorithms—Random Forest and Gradient Boosting Machine (GBM)—in predicting heart disease risk based on patient medical data. A quantitative approach was used, incorporating Exploratory Data Analysis (EDA), data preprocessing, modeling, and evaluation using metrics such as accuracy, precision, recall, and F1-score. The dataset was obtained from Kaggle and included clinical attributes such as age, gender, blood pressure, cholesterol level, and chest pain type. The results show that both algorithms achieved high classification performance, with GBM outperforming overall, achieving 98.3% accuracy, 97.4% precision, 99.4% recall, and 98.4% F1-score. Meanwhile, Random Forest demonstrated strong performance with an accuracy of 94.7%. The most influential features in prediction were ST slope, oldpeak, and chest pain type. This study concludes that the application of GBM is more effective in supporting early heart disease detection and can serve as a fast, accurate, and efficient decision support system in healthcare settings with limited computational resources.]]>
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