IoT Based Smart Health Monitoring System for Diabetes Patients Using Neural Network

Efat, Md. Iftekharul Alam; Rahman, Shoaib; Rahman, Tasnim

doi:10.1007/978-3-030-52856-0_47

Cited by 19 publications

(11 citation statements)

References 14 publications

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“…e results suggest that the current study of training a regression model shows statistically valuable results [26]. In the case of doctors, the results were not statistically signi cant, which suggests that the accuracy levels of doctors vary among them [27]. Not all doctors were highly significant; however, in the case of IoT devices, the complete result was signi cant.…”

Section: Discussion and Findingsmentioning

confidence: 73%

An Investigation in Applying Internet of Things Approach in Safe Food Dietary Plan for Better Chronic Diabetes Management among Elderly Adults

Geetha

Krishna

Vyas

et al. 2022

Journal of Food Quality

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Chronic diabetes among adults is a public health concern and clinicians are trying to implement new strategies to effectively manage the disease. Traditionally, healthcare professionals are used to monitor and track the lab reports of patients. After that, they used to provide respective medicines and lifestyle plans to manage the chronic disease. The lifestyle of the patients and access to safe and secure food products is also responsible for developing chronic diseases. Thus, the Internet of Things (IoT) has taken an utmost interest in managing diabetes. This research is going to analyze the accuracy of IoT in assisting chronic diabetes management and determining food safety. To accomplish the research objectives, the researchers performed a linear regression analysis to understand whether IoT devices and Artificial Intelligence (AI) assist in assessing food safety and diabetes management. The independent variables selected were lab test values, treatment records, epoch size of AI, and image resolution of the training dataset. Dependent variables were the accuracy of IoT. Here, the accuracy of IoT and AI has been determined. Moreover, the accuracy of clinicians in diabetes management has been observed. It has been found that clinicians have high variance in accuracy (max 99%) whereas machines have limited variance in accuracy (max. 98%). Secondary research identified that clinicians need to be involved along with IoT devices for better management of this chronic disease and help patients by providing the safest food options.

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Section: Discussion and Findingsmentioning

confidence: 73%

An Investigation in Applying Internet of Things Approach in Safe Food Dietary Plan for Better Chronic Diabetes Management among Elderly Adults

Geetha

Krishna

Vyas

et al. 2022

Journal of Food Quality

View full text Add to dashboard Cite

show abstract

“…Firstly, ACO is used to optimize the neural network weights as a whole to overcome the shortage of BP algorithm which is easy to fall into local optimum [ 8 , 9 ]. Then taking the better weight as the initial value, BP algorithm is used to do further optimization, to overcome the shortcomings of a single ACO training network with long time and low precision [ 10 – 12 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Design of the Exercise Load Data Monitoring System for Exercise Training Based on the Neural Network

Qin

Feng

2021

Journal of Healthcare Engineering

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In order to monitor the sports load data of athletes in sports training, this paper studies the methods and systems of sports load monitoring and fatigue warning based on neural network technology. In this paper, the neural network parallel optimization algorithm based on big data is used to accurately estimate the motion load and intensity according to the determined motion mode and acceleration data, so as to realize the real-time monitoring of the exercise training. The results show that the value of η is usually small to ensure that the weight correction can truly follow the direction of the gradient descent. In this paper, 176 samples were extracted from the monitoring data collected by the “National Tennis Team Information Platform,” 160 of which were selected as training samples and the other 16 as test samples. Ant colony size M = 20. The minimum value Wmin of the weight interval is −2, and the maximum value Wmax is 2. The maximum number of iterations is set to 200. σ = 1; that is, only one optimal solution is retained. The domain is divided into 60 parts evenly; that is, r = 60. Generally, η can be taken as any number [28] between [10-3, 10], but the value is usually small to ensure that the weight correction can truly follow the direction of the gradient descent. In this paper, the value is 0.003. In the early warning stage of exercise fatigue, reasonable measurement units of exercise fatigue time were divided according to the characteristics of different exercise items. It is proved that the Bayesian classification algorithm can effectively avoid the sports injury caused by overtraining by warning the fatigue and preventing the sports injury caused by overtraining.

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“…The study by Efat et al [ 26 ] introduced a smart health monitoring tool for patients with diabetes. The objective of the authors was to use continuous sensor monitoring and processing with neural networks to provide a continuous evaluation of the patient health risk status.…”

Section: Resultsmentioning

confidence: 99%

Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review

Triantafyllidis¹,

Kondylakis²,

Katehakis³

et al. 2022

JMIR Mhealth Uhealth

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Background Major chronic diseases such as cardiovascular disease (CVD), diabetes, and cancer impose a significant burden on people and health care systems around the globe. Recently, deep learning (DL) has shown great potential for the development of intelligent mobile health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere. Objective The aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field. Methods A search was conducted on the bibliographic databases Scopus and PubMed to identify papers with a focus on the deployment of DL algorithms that used data captured from mobile devices (eg, smartphones, smartwatches, and other wearable devices) targeting CVD, diabetes, or cancer. The identified studies were synthesized according to the target disease, the number of enrolled participants and their age, and the study period as well as the DL algorithm used, the main DL outcome, the data set used, the features selected, and the achieved performance. Results In total, 20 studies were included in the review. A total of 35% (7/20) of DL studies targeted CVD, 45% (9/20) of studies targeted diabetes, and 20% (4/20) of studies targeted cancer. The most common DL outcome was the diagnosis of the patient’s condition for the CVD studies, prediction of blood glucose levels for the studies in diabetes, and early detection of cancer. Most of the DL algorithms used were convolutional neural networks in studies on CVD and cancer and recurrent neural networks in studies on diabetes. The performance of DL was found overall to be satisfactory, reaching >84% accuracy in most studies. In comparison with classic machine learning approaches, DL was found to achieve better performance in almost all studies that reported such comparison outcomes. Most of the studies did not provide details on the explainability of DL outcomes. Conclusions The use of DL can facilitate the diagnosis, management, and treatment of major chronic diseases by harnessing mHealth data. Prospective studies are now required to demonstrate the value of applied DL in real-life mHealth tools and interventions.

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IoT Based Smart Health Monitoring System for Diabetes Patients Using Neural Network

Cited by 19 publications

References 14 publications

An Investigation in Applying Internet of Things Approach in Safe Food Dietary Plan for Better Chronic Diabetes Management among Elderly Adults

An Investigation in Applying Internet of Things Approach in Safe Food Dietary Plan for Better Chronic Diabetes Management among Elderly Adults

Design of the Exercise Load Data Monitoring System for Exercise Training Based on the Neural Network

Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review

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