Cloud-centric IoT based disease diagnosis healthcare framework

In this study, the latest research articles which are involved in the Internet of Things (IoT) based healthcare system are analyzed as the IoT is growing enormously in the healthcare systems such as health monitoring, fitness programs, etc. Numerous research has been carried out in the IoT based healthcare system to improve monitoring efficiency. The architecture used in the IoT especially the cloud integrated systems are investigated in this work. The factors such as accuracy and power consumption are the important concern in the IoT, hence the research works which are involved in improving the performance of the IoT based healthcare systems are discussed. Data management methods in the IoT based healthcare system with cloud facilities are also systematically analyzed in this study. The performance of the IoT based healthcare system along with its advantages and limitations are reviewed. Most research works are efficient in detecting several symptoms and can accurately predict the diseases. The IoT based healthcare system designed especially for elders is an efficient solution in monitoring their healthcare issues. Major limitations in the existing systems are high power consumption, availability of fewer resources and security issues due to the utilization of many devices.

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“…This process as shown in Fig. 2 [19]. The research [20] developed the hierarchical computing architecture (HiCH) Fig.…”

Section: Cloud Integrationmentioning

confidence: 99%

“…Cloud integration with Healthcare IoT system (Source: [19], p. 29) for a patient monitoring system that involves autonomous data management and processing at the edge of the layer.…”

Section: Figmentioning

confidence: 99%

Challenges and opportunities in IoT healthcare systems: a systematic review

Selvaraj¹,

2019

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“…Prabal Verma et al [19,20] proposed a cloud-centric IoT based smart m-healthcare monitoring framework for students. Their proposed framework predicts disease severity level by comparison with health measurement taken from IoT devices and medical domain.…”

Section: State Of the Artmentioning

confidence: 99%

An IoT-Based Intelligent Wound Monitoring System

et al. 2019

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Clinical research of wound assessment focused on physical appearance of wound i.e. wound width, shape, color etc. Although, wound appearance is most crucial factors to influence healing process. however, apart from wound appearance other factors also contribute in healing process. Wound internal and external environment is one such factor that may show positive or negative impact on healing. Internet of things extensively popular during last decade, due to its heavy applications in almost all domains i.e. agriculture, health, marketing, banking, home etc. Therefore, in current research we proposed IoT based intelligent wound assessment system, for assessment of wound status and apply entropy and information gain statistics of decision tree to reflect status of wound assessment by categorization of assessment results in one of three class i.e. good, satisfactory or alarming. We implemented decision tree in MATLAB, in which we select ID3 algorithm for decision tree which based on entropy and information gain for the selection of best feature to split the tree. The efficient feature split of decision tree improved training accuracy rate and performance of decision tree.

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“…A Cloud‐centric and IoT‐based framework for students' healthcare was proposed to predict the potential disease with its level of severity evaluated . The big data generated by the IoT devices in the healthcare domain were analyzed on the Cloud instead of solely relying on hand‐held devices with limited storage and computation resources.…”

Section: Related Workmentioning

confidence: 99%

“…A Cloud-centric and IoT-based framework for students' healthcare was proposed to predict the potential disease with its level of severity evaluated. 32 The big data generated by the IoT devices in the healthcare domain were analyzed on the Cloud instead of solely relying on hand-held devices with limited storage and computation resources. The results for infectious and heart diseases were eventually given by a Decision tree (C 4.5) model, and the framework achieved an accuracy of 92.8%, sensitivity of 90.4%, specificity of 93.3%, and F-measure of 96%, respectively.…”

Section: Brain Healthcare Systemmentioning

confidence: 99%

Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN

Dan

Shah

et al. 2018

Softw Pract Exp

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Summary Brain healthcare, when supported by Internet of Things, can perform online and accurate analysis of brain big data for the classification of multivariate Electroencephalogram (EEG), which is a prerequisite for the recent boom in neurofeedback applications and clinical practices. However, it remains a grand research challenge due to (1) the embedded intensive noises and the intrinsic nonstationarity determined by the evolution of brain states; and (2) the lack of a user‐friendly computing platform to sustain the complicated analytics. This study presents the design of an online EEG classification system aided by Cloud centering on a lightweight Convolutional Neural Network (CNN). The system incrementally trains the CNN on Cloud and enables hot deployment of the trained classifier without the need to restart the gateway to adapt to the users' needs. The classifier maintains a High Convolutional Layer to gain the ability of processing high‐dimensional EEG segments. The number of hidden layers is minimized to ensure the efficiency of training. The lightweight CNN adopts an “hourglass” block of fully connected layers to reduce the number of neurons quickly toward the output end. A case study of depression evaluation has been performed against raw EEG datasets to distinguish between (1) Healthy and Major Depression Disorder with an accuracy, sensitivity, and specificity of [98.59% ± 0.28%], [97.77% ± 0.63%], and [99.51% ± 0.19%], respectively; and (2) Effective and Noneffective treatment outcome with an accuracy, sensitivity, and specificity of [99.53% ± 0.002%], [99.50% ± 0.01%], and [99.58% ± 0.02%], respectively. The results show that the classification can be completed several magnitudes faster when EEG is collected on the gateway (several milliseconds vs. 4 seconds).

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Cloud-centric IoT based disease diagnosis healthcare framework

Cited by 203 publications

References 33 publications

Challenges and opportunities in IoT healthcare systems: a systematic review

Challenges and opportunities in IoT healthcare systems: a systematic review

An IoT-Based Intelligent Wound Monitoring System

Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN

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