<![CDATA[The heart is a vital organ that functions as a blood pump to meet the body's oxygen and nutrient needs. Disorders of the heart can result in disruptions to blood circulation, underscoring the importance of maintaining heart health to prevent heart disease. The optimal function of the heart depends on the condition of the cardiac muscles, valves, and proper pumping rhythm. Heart failure, as one of the heart diseases, refers to the heart's inability to meet the body's blood demands. The management of heart disease involves comprehensive medical services provided in hospitals. One effort to improve the quality of healthcare services is to enhance the performance of hospitals. The Regional General Hospital of Kota Prabumulih is one healthcare institution that addresses various diseases, including heart disease. However, the classification process for heart disease at the Regional General Hospital is still conducted manually, consuming time and inefficient. To address this issue, the development of an intelligent system capable of classifying heart failure based on patient symptoms is necessary. This research proposes the implementation of the Support Vector Machine (SVM) method as a classification model, which is a high-dimensional hypothesis learning system. It is hoped that this system can assist medical professionals in determining the initial steps in managing heart failure quickly and effectively.]]>
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