<![CDATA[Heart disease is one of the leading causes of death worldwide and is influenced by various risk factors, such as age, cholesterol levels, blood pressure, diabetes, and unhealthy lifestyles. The increasing prevalence of heart disease highlights the need for a prediction system that can support early detection quickly and accurately. This study aims to develop a heart disease prediction model using the Random Forest algorithm based on the Cross Industry Standard Process for Data mining (CRISP-DM) methodology. The dataset consisted of 1,000 patient records with 15 predictor variables and one target variable (heart_disease). The research stages included Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and Area Under Curve (AUC). The results showed that the Random Forest model achieved an Accuracy of 99%, Precision of 100%, Recall of 97.44%, F1-Score of 98.70%, and an AUC of 1.00. Feature importance analysis revealed that age and cholesterol level were the most influential factors in predicting heart disease. The findings indicate that the CRISP-DM-based Random Forest algorithm can produce an accurate prediction model and has the potential to support early detection of heart disease.]]>
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