<![CDATA[Heart disease is one of the leading causes of death worldwide, and early detection is crucial in reducing mortality rates. In Indonesia, heart disease is a primary cause of death, exacerbated by limited access to healthcare, especially in rural areas. Traditional diagnostic methods, such as physical examinations and EKG, often lack accuracy in predicting heart attacks. This research aims to develop an early prediction model for heart attacks using machine learning, specifically Random Forest and Support Vector Machine (SVM). These models were trained using a dataset containing various medical variables, including age, gender, blood pressure, cholesterol levels, and ECG results. The study finds that the Random Forest model outperforms SVM, with an accuracy of 90% and a recall of 93% for heart disease detection, making it more reliable for early detection of at-risk patients. The results suggest that machine learning can significantly enhance early heart attack detection, offering a potential solution to reduce heart disease-related mortality.]]>
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