5G SMART DIABETS: TOWARDS PERSIONALIZED DIABETS DIAGNOSIS WITH HEALTH CARE BIGDATA CLOUDS

Abstract

Recent strides in wireless networking and big data technologies, exemplified by the
advent of 5G networks, medical big data analytics, and the Internet of Things (IoT),
coupled with notable progress in wearable computing and artificial intelligence, have
paved the way for innovative diabetes monitoring systems and applications. Given the
enduring and systemic impact of diabetes on patients, there is an urgent need to devise
effective methods for its diagnosis and treatment. This article conducts a thorough
investigation, categorizing existing methods into Diabetes 1.0 and Diabetes 2.0,
revealing shortcomings in terms of networking and intelligence. Consequently, our
objective is to formulate a sustainable, cost-effective, and intelligent diabetes
diagnosis solution featuring personalized treatment.
In pursuit of this goal, we introduce the 5G-Smart Diabetes system, a groundbreaking
approach that integrates cutting-edge technologies such as wearable 2.0, machine
learning, and big data to facilitate comprehensive sensing and analysis for individuals
affected by diabetes. Furthermore, we delineate the data sharing mechanism and
present a personalized data analysis model tailored for the 5G-Smart Diabetes system.
The culmination of our efforts manifests in the establishment of a 5G-Smart Diabetes
testbed, comprising smart clothing, smartphones, and big data clouds.
Empirical results from our experiments underscore the effectiveness of our system in
furnishing personalized diagnosis and treatment suggestions to patients. The 5G-Smart Diabetes system not only addresses existing deficiencies in diabetes
management but also lays the foundation for an intelligent, interconnected healthcare
ecosystem that prioritizes individualized care and improved outcomes for those
grappling with diabetes.