<![CDATA[Heart disease, also known as coronary heart disease, occurs when blood flow to the heart muscle is reduced or blocked, causing significant damage. The objective of this study is to develop a predictive model that can estimate the risk of heart disease using the Support Vector Machine (SVM) algorithm with a sigmoid kernel, so that patients can be classified into high-risk and low-risk categories. The modeling stage is carried out to select and implement the appropriate modeling technique, determine the data mining tools to be used, and set optimal parameter values. At this stage, the training data are learned by the selected algorithm model, and the testing data are then evaluated using the developed classifier to obtain performance metrics. The results of this study indicate that the SVM method with a sigmoid kernel provides a good level of accuracy in predicting heart disease risk based on measured risk factors such as age, gender, blood pressure, cholesterol levels, and others. From the experiments conducted, the classification performed well. Using 303 data instances that were randomly sampled into 1,220 data points, the model achieved an accuracy of 0.788, a precision of 0.787, and a recall of 0.788.]]>
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