Email this article Smart Hypertension Detection: AI-Powered Blood Pressure Risk Prediction
Abstract
Hypertension is a silent yet deadly condition that often goes undetected until severe complications occur. This project introduces a machine learning-based system to predict hypertension using real-time data from Ambulatory Blood Pressure Monitoring (ABPM). Initially employing a Decision Tree classifier for categorizing blood pressure risk levels, the model is further enhanced with a hybrid ensemble approach combining Random Forest, SVM, and XGBoost for higher accuracy. The system supports real-time predictions and sends automated alerts to healthcare providers. It achieves an impressive accuracy rate of 99.98%, making it highly suitable for scalable preventive care. This work contributes to AI-driven healthcare by offering a cost-effective, accurate, and automated solution for early hypertension detection and patient monitoring.
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