<![CDATA[Hypertension is a major global health concern and a leading risk factor for cardiovascular disease, stroke, and kidney failure. Early prediction of hypertension is crucial because the condition is often asymptomatic in its initial stages and late detection increases the likelihood of severe complications. This study aims to develop and evaluate a predictive model for hypertension using the Random Forest algorithm, a robust ensemble learning method well-suited for medical data classification. The dataset used in this research was obtained from Kaggle and contains 1,985 records with 11 attributes representing demographic, lifestyle, and clinical risk factors. Preprocessing was performed to ensure data quality, followed by Random Forest classification with different parameter settings. The model was evaluated using 5-fold and 10-fold cross-validation with various numbers of trees ranging from 50 to 250. Performance metrics included accuracy, precision, recall, F1-score, and AUC. Experimental results demonstrated that the Random Forest algorithm achieved consistently high performance, with accuracy above 93%, precision above 95%, recall above 91%, F1-scores above 93%, and AUC values between 0.986 and 0.991. These findings confirm that Random Forest is highly effective and reliable for predicting hypertension risk. The study highlights the algorithm’s potential as a decision-support tool for early detection, enabling preventive measures and improving public health outcomes.]]>
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