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The rise of digital healthcare has introduced innovative solutions for chronic disease management, particularly hypertension. This paper explores the implementation of AI-driven blood pressure (BP) monitoring systems that provide personalized healthcare recommendations based on real-time and historical BP data. The study investigates how machine learning (ML) and natural language processing (NLP) enhance BP tracking, predict cardiovascular risks, and generate tailored lifestyle and treatment suggestions. A case study of "BP Genie," a web-based BP monitoring platform, demonstrates the effectiveness of AI in improving patient adherence, reducing manual errors, and delivering actionable insights. Findings indicate that AI-driven BP systems significantly enhance patient outcomes by offering real-time analysis, automated alerts, and personalized diet/exercise plans. The paper concludes with recommendations for optimizing AI integration in telemedicine platforms, ensuring scalability, and addressing data privacy concerns.
Keywords:
AI in healthcare, blood pressure monitoring, personalized medicine, hypertension management, machine learning, NLP, telemedicine, Role-Based Access Control (RBAC)
Cite Article:
"AI-Driven Blood Pressure Monitoring and Personalized Healthcare Recommendations", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.b726-b732, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505181.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator