<![CDATA[Heart failure is one of the cardiovascular diseases that has a significant impact on patients' quality of life and requires appropriate medical treatment. With the advancement of technology, the use of machine learning algorithms to predict the risk of heart failure can enhance the efficiency of diagnosis and treatment. This study aims to compare the performance of five machine learning algorithms in predicting heart failure in patients. The algorithms used in this study are K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Naïve Bayes, and Deep Learning. The dataset contains patients' medical data, including medical history, symptoms, and clinical test results. The evaluation method was carried out by measuring the accuracy, precision, recall, and F1-score of each algorithm. The results show that the Random Forest algorithm achieved the best performance in terms of accuracy and prediction stability, followed by Deep Learning and Naïve Bayes.]]>
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