<![CDATA[Heart disease remains one of the leading causes of mortality worldwide, highlighting the need for accurate prediction models to support early diagnosis and clinical decision-making. This study proposes a heart disease risk prediction model based on Support Vector Machine (SVM) integrated with Recursive Feature Elimination (RFE) for feature selection and GridSearchCV for hyperparameter optimization. The study utilized the Cleveland Heart Disease Dataset, consisting of 303 patient records, 13 predictive attributes, and one target variable. The research workflow included dataset collection, data preprocessing, feature selection, hyperparameter optimization, model development, and performance evaluation using Accuracy, Precision, Recall, F1-score, and ROC-AUC. Experimental results demonstrate that the proposed model achieved an Accuracy of 86.89%, Precision of 85.71%, Recall of 85.71%, F1-score of 85.71%, and ROC-AUC of 93.18%. Feature selection successfully reduced irrelevant attributes, improving model efficiency, while hyperparameter optimization produced a more effective parameter configuration than the default settings. These findings indicate that integrating RFE with GridSearchCV enhances the predictive performance of SVM and provides a promising approach for supporting heart disease diagnosis using machine learning techniques.]]>
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