DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks

Priyadarshini, Rojalina; Barik, Rabindra K.; Dubey, Harishchandra

doi:10.3390/computation6040062

Cited by 39 publications

(17 citation statements)

References 69 publications

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“…Multilayer perceptron (MLP), auto-encoder (AE), convolutional neural network (CNN), recurrent neural network (RNN), restricted Boltzmann machine (RBM), neural autoregressive distribution estimation and adversarial networks (AN) are the main components of the deep learning method [10,33,[47][48][49].…”

Section: Deep Learning Methodsmentioning

confidence: 99%

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

et al. 2019

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In today’s digital world healthcare is one core area of the medical domain. A healthcare system is required to analyze a large amount of patient data which helps to derive insights and assist the prediction of diseases. This system should be intelligent in order to predict a health condition by analyzing a patient’s lifestyle, physical health records and social activities. The health recommender system (HRS) is becoming an important platform for healthcare services. In this context, health intelligent systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of the valuable information at the right time by ensuring information quality, trustworthiness, authentication and privacy concerns. As people use social networks to understand their health condition, so the health recommender system is very important to derive outcomes such as recommending diagnoses, health insurance, clinical pathway-based treatment methods and alternative medicines based on the patient’s health profile. Recent research which targets the utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed which reduces the workload and cost in health care. In the healthcare sector, big data analytics using recommender systems have an important role in terms of decision-making processes with respect to a patient’s health. This paper gives a proposed intelligent HRS using Restricted Boltzmann Machine (RBM)-Convolutional Neural Network (CNN) deep learning method, which provides an insight into how big data analytics can be used for the implementation of an effective health recommender engine, and illustrates an opportunity for the health care industry to transition from a traditional scenario to a more personalized paradigm in a tele-health environment. By considering Root Square Mean Error (RSME) and Mean Absolute Error (MAE) values, the proposed deep learning method (RBM-CNN) presents fewer errors compared to other approaches.

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Section: Deep Learning Methodsmentioning

confidence: 99%

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

et al. 2019

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“…In another study [71], decision trees were used for the classification of diabetes risk level, and the smartphone was used as an edge device. Another DL-based system was proposed in [72] to predict stress, hypertension, and diabetes using wearable body sensors, and it was implemented using the fog layer.…”

Section: E Diabetes Treatmentmentioning

confidence: 99%

Edge Intelligence and Internet of Things in Healthcare: A Survey

2021

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“…For example, Abdel-Basset et al [6] discuss a novel technique to predict type-2 diabetes risks, while [17] use a J48Graft decision tree classifier, implemented on a smartphone, to discover the risk level of diabetic patients. Finally, a deep learning model to predict diabetes, stress types and hypertension attacks from wearable sensor data is discussed in Priyadarshini et al [42] , where a fog architecture is also proposed.…”

Section: Diabetes Treatmentmentioning

confidence: 99%

Trends in IoT based solutions for health care: Moving AI to the edge

Greco

Percannella

Ritrovato

et al. 2020

Pattern Recognition Letters

245

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In recent times, we assist to an ever growing diffusion of smart medical sensors and Internet of things devices that are heavily changing the way healthcare is approached worldwide. In this context, a combination of Cloud and IoT architectures is often exploited to make smart healthcare systems capable of supporting near realtime applications when processing and performing Artificial Intelligence on the huge amount of data produced by wearable sensor networks. Anyway, the response time and the availability of cloud based systems, together with security and privacy, still represent critical issues that prevents Internet of Medical Things (IoMT) devices and architectures from being a reliable and effective solution to the aim. Lately, there is a growing interest towards architectures and approaches that exploit Edge and Fog computing as an answer to compensate the weaknesses of the cloud. In this paper, we propose a short review about the general use of IoT solutions in health care, starting from early health monitoring solutions from wearable sensors up to a discussion about the latest trends in fog/edge computing for smart health.

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DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks

Cited by 39 publications

References 69 publications

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

Edge Intelligence and Internet of Things in Healthcare: A Survey

Trends in IoT based solutions for health care: Moving AI to the edge

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