Machine Learning-Based Personalized Blood Pressure Control for Remote Patient Monitoring

Abstract

Personalized blood pressure control has become a critical focus in modern healthcare due to the rising prevalence of hypertension and its associated complications such as cardiovascular diseases and stroke. Traditional treatment approaches often rely on generalized guidelines, which may not effectively address individual patient variability. This paper proposes a machine learning-based approach for personalized blood pressure management within remote patient monitoring systems. By leveraging patient-specific data such as historical blood pressure readings, lifestyle patterns, medication adherence, and physiological parameters, machine learning models can predict trends and recommend optimized interventions. The integration of wearable devices and IoT-enabled health monitoring systems allows continuous data collection and real-time analysis. This approach enhances early detection of anomalies, improves treatment accuracy, and supports proactive healthcare delivery. The proposed system aims to reduce hospital visits, improve patient outcomes, and enable efficient healthcare management through intelligent decision support.