<![CDATA[Hypertension is a chronic disease that serves as a major risk factor for cardiovascular disorders and requires early detection to prevent serious complications. This study aims to develop an Artificial Neural Network (ANN) model for binary classification of individuals with and without hypertension. The training data were used to train the model while monitoring the accuracy and validation loss metrics, whereas model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the ANN model achieved an accuracy of 87.8%, demonstrating a balanced ability to identify both hypertensive and non-hypertensive patients. The confusion matrix shows a high number of correct predictions in both classes, confirming the model’s effectiveness in supporting binary classification for health data. These findings suggest that ANN has the potential to serve as an effective approach to support medical decision support systems, particularly in the early detection of hypertension and in reducing long-term complication risks through more timely interventions.]]>
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