Internet of things-inspired healthcare system for urine-based diabetes prediction

Bhatia, Munish; Kaur, Simranpreet; Sood, Sandeep K.; Behal, Veerawali

doi:10.1016/j.artmed.2020.101913

Cited by 55 publications

(21 citation statements)

References 28 publications

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“…Recently, several studies have been conducted for RHM for diabetic patients based on mHealth technologies. These studies have focused on several related topics including diabetic management and control [15], [16], diabetes prevention [17], diabetes intervention program [18], diabetes self-efficacy [19], continuous glucose monitoring [20], glycemic control improvement [21], diabetic patients treatment [22], diabetes prediction system [23], diabetes care improvement [24], continuous and remote monitoring system [25], insulin dose management [26], and carbohydrate measurement [27]. However, Tables 1, 2, and 3 show existing mHealth studies for managing, diagnosing, tracking, detecting, and predicting diabetic mellitus, respectively.…”

Section: Related Workmentioning

confidence: 99%

Mobile health monitoring based studies for diabetes mellitus: a review

AlShorman

Masadeh

Alshorman³

2021

Bulletin EEI

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Diabetes as a chronic disease is considered to be a serious problem not only for diabetic patients but also for caregivers, families and countries. Hazardously, as an example, 16% of the Middle East population died every year because of diabetes as it is reported by World Health Organization (WHO). Therefore, it is crucial to utilize the recent advances and technologies to find the best instrument for diabetes monitoring and management. Recently, mobile health (mHealth) technologies have a vital role in the healthcare industrial world. Undoubtedly, mHealth technologies are used to manage, track, monitor, diagnose, and prevent chronic diseases including, diabetes. Certainly, the main advantages of mHealth include a real-time and continuous monitoring with high reliability, accessibility, and availability. In addition to that, mHealth is considered to be a fast, accurate, simple, cheap, comfortable, and safe technology. Hence, the proposed study aims to review existing mHealth studies for managing, diagnosing, tracking, detecting, and predicting diabetic mellitus. Moreover, challenges and future trends of this emerging topic are also discussed.

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Section: Related Workmentioning

confidence: 99%

Mobile health monitoring based studies for diabetes mellitus: a review

AlShorman

Masadeh

Alshorman³

2021

Bulletin EEI

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“… 98 Bhatia et al (2020) demonstrated an effective IoMT based home-centric Urine-based Diabetes (UbD) monitoring system using Recurrent Neural Network (RNN). 99 …”

Section: Applications Of Iomt In Healthcarementioning

confidence: 99%

Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review

Dwivedi

Mehrotra

Chandra

2022

Journal of Oral Biology and Craniofacial Research

201

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Sudden spurting of Corona virus disease (COVID-19) has put the whole healthcare system on high alert. Internet of Medical Things (IoMT) has eased the situation to a great extent, also COVID-19 has motivated scientists to make new ‘Smart’ healthcare system focusing towards early diagnosis, prevention of spread, education and treatment and facilitate living in the new normal. This review aims to identify the role of IoMT applications in improving healthcare system and to analyze the status of research demonstrating effectiveness of IoMT benefits to the patient and healthcare system along with a brief insight into technologies supplementing IoMT and challenges faced in developing a smart healthcare system. An internet-based search in PUBMED, Google Scholar and IEEE Library for english language publications using relevant terms resulted in 987 articles. After screening title, abstract, and content related to IoMT in healthcare and excluding duplicate articles, 135 articles published in journal with impact factor ≥1 were eligible for inclusion. Also relevant articles from the references of the selected articles were considered. The habituation of IoMT and related technology has resolved several difficulties using remote monitoring, telemedicine, robotics, sensors etc. However mass adoption seems challenging due to factors like privacy and security of data, management of large amount of data, scalability and upgradation etc. Although ample knowledge has been compiled and exchanged, this structured systematic review will help the healthcare practitioners, policymakers/decision makers, scientists and researchers to gauge the applicability of IoMT in healthcare more efficiently.

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“…A practical framework (Bhatia et al, 2020) has been developed for forecasting home-centric urine-based diabetes. It has four different layers: Diabetic information collection, diabetic information categorization, diabeticextraction and diabetic estimation and decision-making layers to predict and forecast the diabetes-oriented urine virus.…”

Section: Literature Surveymentioning

confidence: 99%

Efficient Handling of Medical Data Classification in Cloud-Edge Network using Optimization Algorithm

Saranya¹,

Fatima²

2021

Journal of Computer Science

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Nowadays, a cloud-edge computing framework with IoT offers different medicinal facilities by classifying a massive amount of patients' health data through a Deep Neural Network. But, how to optimize task scheduling while carrying multiple tasks from multiple edge devices in realtime was still challenging. This article introduces a cooperative cloud-edge computing structure to effectively perform the fuzzy DNN classification into the edge system and handle the computationally complicated tasks of DNNs. First, the edge servers are constructed with fuzzy DNNs and cooperate with the cloud to create a cooperative cloud-edge computing paradigm. Then, an adaptive deployment method is developed using a Lion Optimization Algorithm, which supports the cloud to decide which task will be executed at the edge devices. Therefore, the study of fuzzy DNN using health data is performed for forecasting and diagnosing various diseases. Finally, the simulation outcomes reveal that the LOA achieved 37.8Jin energy use and 17.8ms latency while using 25 edge devices. Also, the fuzzy DNN achieved 85.8% accuracy for classifying the medical data and diagnosing them in the earlier stage. It concludes that LOA and fuzzy DNN are more efficient than classical optimization and classification for healthcare applications using the cloud-edge computing paradigm.

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Internet of things-inspired healthcare system for urine-based diabetes prediction

Cited by 55 publications

References 28 publications

Mobile health monitoring based studies for diabetes mellitus: a review

Mobile health monitoring based studies for diabetes mellitus: a review

Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review

Efficient Handling of Medical Data Classification in Cloud-Edge Network using Optimization Algorithm

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