<![CDATA[Stroke is one of the diseases that significantly affects health and economy, becoming the second most common cause of death in the world after coronary heart disease. Based on data from the World Health Organization (WHO), stroke is ranked second as the leading cause of death in the world after ischemic heart disease. In 2019, stroke was responsible for around 11% of total global deaths. One important way to reduce the death rate from stroke is to make prevention efforts through early prediction. Machine learning methods, especially Random Forest, are used in this study to predict the risk of stroke. The data used comes from a public dataset that includes age, gender, blood pressure, blood sugar, smoking status, and other medical history. The research process includes data pre-processing stages (data cleaning, outlier handling, and category coding), model training using the Random Forest algorithm, and model evaluation using a confusion matrix to evaluate accuracy, precision, recall, and F1 score. The evaluation results show an accuracy value of 97.55%, which indicates very good predictive performance so that this model has very good predictive performance.]]>
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