5G-Smart Diabetes: Toward Personalized Diabetes Diagnosis with Healthcare Big Data Clouds

Chen, Min; Yang, Jun; Zhou, Jiehan; Hao, Yixue; Zhang, Jing; Youn, Chan‐Hyun

doi:10.1109/mcom.2018.1700788

Cited by 219 publications

(84 citation statements)

References 11 publications

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“…In [7], M. Chen et al propose a mobile health system using 5G for constant assessment and monitoring of diabetes patients. First, the authors present the 5G-Smart Diabetes system combining existing technologies such as Wearable 2.0, machine learning, and big data for creating comprehensive monitoring and analysis for diabetic patients.…”

Section: Related Workmentioning

confidence: 99%

A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms

et al. 2020

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Continuous monitoring of diabetic patients improves their quality of life. The use of multiple technologies such as the Internet of Things (IoT), embedded systems, communication technologies, artificial intelligence, and smart devices can reduce the economic costs of the healthcare system. Different communication technologies have made it possible to provide personalized and remote health services. In order to respond to the needs of future intelligent e-health applications, we are called to develop intelligent healthcare systems and expand the number of applications connected to the network. Therefore, the 5G network should support intelligent healthcare applications, to meet some important requirements such as high bandwidth and high energy efficiency. This article presents an intelligent architecture for monitoring diabetic patients by using machine learning algorithms. The architecture elements included smart devices, sensors, and smartphones to collect measurements from the body. The intelligent system collected the data received from the patient, and performed data classification using machine learning in order to make a diagnosis. The proposed prediction system was evaluated by several machine learning algorithms, and the simulation results demonstrated that the sequential minimal optimization (SMO) algorithm gives superior classification accuracy, sensitivity, and precision compared to other algorithms.

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Section: Related Workmentioning

confidence: 99%

A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms

et al. 2020

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“…Previous studies have introduced predictive models for diseases such as diabetic retinopathy, skin cancer, lung disease, heart failure, chronic kidney disease, and so on using machine learning techniques [14][15][16][17][18][19][20]. These studies that use deep learning techniques to make major advances in solving problems have resisted the best attempts of the artificial intelligence community in many cases [21].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes

et al. 2020

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A screening model for undiagnosed diabetes mellitus (DM) is important for early medical care. Insufficient research has been carried out developing a screening model for undiagnosed DM using machine learning techniques. Thus, the primary objective of this study was to develop a screening model for patients with undiagnosed DM using a deep neural network. We conducted a cross-sectional study using data from the Korean National Health and Nutrition Examination Survey (KNHANES) 2013–2016. A total of 11,456 participants were selected, excluding those with diagnosed DM, an age < 20 years, or missing data. KNHANES 2013–2015 was used as a training dataset and analyzed to develop a deep learning model (DLM) for undiagnosed DM. The DLM was evaluated with 4444 participants who were surveyed in the 2016 KNHANES. The DLM was constructed using seven non-invasive variables (NIV): age, waist circumference, body mass index, gender, smoking status, hypertension, and family history of diabetes. The model showed an appropriate performance (area under curve (AUC): 80.11) compared with existing previous screening models. The DLM developed in this study for patients with undiagnosed diabetes could contribute to early medical care.

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“…(Alansari, 2018). On the other hand, digital wearable technologies are also useful for exercise monitoring, heart rate monitoring, female health monitoring, body temperature, blood pressure, sleep cycle monitoring and control, calorie burnt (Lee and Ouyang, 2014) as well as monitoring of blood sugar level (Deshkar et al, 2017;Chen et al, 2018). This chapter tries to highlight and understand the managerial problem linked to assurance of personalized care service delivery and aims at explaining and establishing the logical linkages between how big-data capabilities and IoT enabled cloud-platform helps in achieving superior patient care monitoring.…”

Section: Introductionmentioning

confidence: 99%

Big Data, IoT Wearable and m Health Cloud Platform Integration Triads a Logical Way to Patient Health Monitoring

Chakraborty¹,

Bhatt²,

Chakravorty³

2020

IJEAT

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IoT along with big data capabilities is useful in fall detection, medical fridges, sportsman care, patient surveillance, chronic disease management, sleep control and monitoring, etc. Every year a large number of patients are identified with diabetes or cardiac disorders. There is a greater need to handle many patients with the existing medical staff and doctors like cardiologists and diabetologists. This chapter aims at establishing the logical conceptualization and linkages of IoT enabled system linkages with wearable, big-data platforms and cloud-based mhealth delivery. The study subsequently aims at qualitatively and quantitatively validating and putting forth a feasible nuanced understanding framework linking the major contemporary technology triads/automated care delivery process platform in the healthcare context with prime focus on patient health monitoring and care-delivery

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5G-Smart Diabetes: Toward Personalized Diabetes Diagnosis with Healthcare Big Data Clouds

Cited by 219 publications

References 11 publications

A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms

A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms

A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes

Big Data, IoT Wearable and m Health Cloud Platform Integration Triads a Logical Way to Patient Health Monitoring

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