Ensemble Approach for Hypertension Risk Prediction Using Clinical and Demographic Features

Hypertension, also known as high blood pressure, is a major risk factor for cardiovascular diseases and stroke, and it often progresses silently until severe complications arise. Early detection is therefore essential for timely management and prevention. Traditional screening methods, however, do not always integrate multiple risk factors for accurate and early identification. This study develops a hypertension prediction system using deep learning and ensemble machine-learning techniques based on a dataset containing demographic, clinical, and lifestyle features. A Multi-Layer Perceptron (MLP), Random Forest, and XGBoost were trained and evaluated, with the Random Forest achieving an accuracy of 87.13%, XGBoost 84.50%, and the MLP 76.28%. An ensemble of the three models achieved 94% accuracy, indicating improved stability and predictive capability. While the system performs well, limitations such as possible overfitting and population-specific bias are noted. The study contributes to AI-driven healthcare by demonstrating a practical approach for early hypertension risk prediction. Future work may involve expanding the dataset, incorporating additional clinical indicators, and improving model robustness across diverse populations.