Machine Learning for Smart Healthcare Management Using IoT

Yigit, Yagmur; Duran, Kubra; Moradpoor, Naghmeh; Maglaras, Leandros; Van Huynh, Nguyen; Canberk, Berk

Abstract

The convergence of Machine Learning (ML) and the Internet of Things (IoT) has brought about a paradigm shift in healthcare, ushering in a new era of intelligent healthcare management. This powerful amalgamation is driving transformative changes across the healthcare landscape, offering solutions that encompass remote patient monitoring, telemedicine, and the efficient handling of vast volumes of medical data. At the forefront of this transformation is IoT-driven remote patient monitoring, a game-changer in healthcare. Enabled by wearable sensors and smart devices, it empowers patients and healthcare providers alike. These devices facilitate the continuous collection of real-time health data, creating a comprehensive and up-to-the-minute view of an individual's well-being. This data empowers healthcare providers to deliver truly personalized care and timely interventions, fundamentally changing the way healthcare is delivered. The backbone of this revolution is ML. Its algorithms tirelessly process and analyze the deluge of health data, yielding invaluable insights, predictive capabilities, and pattern recognition. These are indispensable for early disease detection and the tailoring of treatment recommendations to individual patients. However, in this data-rich era, the paramount concern remains safeguarding data privacy and security.
Looking ahead, the horizon is filled with promising opportunities. The integration of digital twin technology stands as a beacon for personalized medicine. By creating virtual replicas of patients, healthcare providers gain unprecedented insights into individual health profiles, leading to more precise diagnoses and treatment plans. In tandem, blockchain technology, with its inherent security features, holds the potential to fortify data integrity and privacy within healthcare records, cementing trust and security in this data-driven healthcare landscape. The ever-evolving realm of Artificial Intelligence (AI) continues to offer both opportunities and challenges. From advanced ML models capable of intricate medical analyses to natural language processing and predictive analytics that optimize healthcare workflows and diagnoses, AI is redefining the boundaries of healthcare. ML-driven healthcare solutions harnessed through IoT are at the forefront of innovation. They enable individuals to actively manage their health while providing healthcare providers with the tools necessary for timely interventions, ultimately leading to improved patient outcomes. To unlock the full potential of this convergence, ongoing efforts should be laser-focused on enhancing healthcare accessibility, reducing costs, and elevating the standard of patient care. The research and advancements highlighted in this chapter underscore the profound impact of this integration, setting the stage for a smarter, more effective healthcare future. Embracing future technologies and fortifying security measures will be pivotal in realizing a patient-centric and secure healthcare ecosystem.

Citation

Yigit, Y., Duran, K., Moradpoor, N., Maglaras, L., Van Huynh, N., & Canberk, B. (in press). Machine Learning for Smart Healthcare Management Using IoT. In IoT and ML for Information Management: A Smart Healthcare Perspective. Springer

• Acceptance Date: Oct 28, 2023
• Deposit Date: Oct 30, 2023
• Publisher: Springer
• Book Title: IoT and ML for Information Management: A Smart Healthcare Perspective
• Chapter Number: 5
• Keywords: Machine Learning, Smart Healthcare, IoT, Healthcare Management, Remote Healthcare Monitoring, Telemedicine Systems, Medical Big Data, Sensors, Data Privacy, Data Security, Personalized Healthcare
• Public URL: http://researchrepository.napier.ac.uk/Output/3226166