Comparative Analysis of e-Health Care Telemedicine System Based on Internet of Medical Things and Artificial Intelligence

Kadu, Ankush; Singh, Manwinder

doi:10.1109/icosec51865.2021.9591941

Cited by 19 publications

(26 citation statements)

References 51 publications

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“…The current system records the user’s walking activity and stores the route, but it does not link the route recorded to the number of calories burned. The author of [ 22 ] mentions that a literature review of the work has been done, focusing on the benefits of merging telemedicine with AI. These advantages present endless growth opportunities.…”

Section: Literature Surveymentioning

confidence: 99%

An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications

Menon

Shukla

Sethi

et al. 2023

Sensors

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Background: Continuous surveillance helps people with diabetes live better lives. A wide range of technologies, including the Internet of Things (IoT), modern communications, and artificial intelligence (AI), can assist in lowering the expense of health services. Due to numerous communication systems, it is now possible to provide customized and distant healthcare. Main problem: Healthcare data grows daily, making storage and processing challenging. We provide intelligent healthcare structures for smart e-health apps to solve the aforesaid problem. The 5G network must offer advanced healthcare services to meet important requirements like large bandwidth and excellent energy efficacy. Methodology: This research suggested an intelligent system for diabetic patient tracking based on machine learning (ML). The architectural components comprised smartphones, sensors, and smart devices, to gather body dimensions. Then, the preprocessed data is normalized using the normalization procedure. To extract features, we use linear discriminant analysis (LDA). To establish a diagnosis, the intelligent system conducted data classification utilizing the suggested advanced-spatial-vector-based Random Forest (ASV-RF) in conjunction with particle swarm optimization (PSO). Results: Compared to other techniques, the simulation’s outcomes demonstrate that the suggested approach offers greater accuracy.

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Section: Literature Surveymentioning

confidence: 99%

An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications

Menon

Shukla

Sethi

et al. 2023

Sensors

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“…Vardhini et al [91] proposed a blockchain and smart contract-based framework to resolve the problem of EMRs, i.e., misuse of data and security, which has been solved by providing data privacy, interportability, and accessibility. Kadu and Singh [32] proposed an SCs-based transaction prioritization architecture for digital healthcare. It provides creative alternative solutions with intelligent aid diagnosis and continuous monitoring facility.…”

Section: Mhealthmentioning

confidence: 99%

“…Yang et al [31] reviewed IoT-enabled mobile healthcare technologies along with studying detailed smart health monitoring systems and types of sensor devices used along with IoT. Kadu and Singh [32] surveyed e-Healthcare telemedicine systems based on the Internet of Medical Things (IoMT) and AI, which facilitates in managing information creating significant improvements in the global health sector, especially telemedicine. Balakrishnan [13] presented a survey on IoT-based intelligent framework for healthcare.…”

Section: Introductionmentioning

confidence: 99%

A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions

et al. 2022

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There is a massive transformation in the traditional healthcare system from the specialist-centric approach to the patient-centric approach by adopting modern and intelligent healthcare solutions to build a smart healthcare system. It permits patients to directly share their medical data with the specialist for remote diagnosis without any human intervention. Furthermore, the remote monitoring of patients utilizing wearable sensors, Internet of Things (IoT) technologies, and artificial intelligence (AI) has made the treatment readily accessible and affordable. However, the advancement also brings several security and privacy concerns that poorly maneuvered the effective performance of the smart healthcare system. An attacker can exploit the IoT infrastructure, perform an adversarial attack on AI models, and proliferate resource starvation attacks in smart healthcare system. To overcome the aforementioned issues, in this survey, we extensively reviewed and created a comprehensive taxonomy of various smart healthcare technologies such as wearable devices, digital healthcare, and body area networks (BANs), along with their security aspects and solutions for the smart healthcare system. Moreover, we propose an AI-based architecture with the 6G network interface to secure the data exchange between patients and medical practitioners. We have examined our proposed architecture with the case study based on the COVID-19 pandemic by adopting unmanned aerial vehicles (UAVs) for data exchange. The performance of the proposed architecture is evaluated using various machine learning (ML) classification algorithms such as random forest (RF), naive Bayes (NB), logistic regression (LR), linear discriminant analysis (LDA), and perceptron. The RF classification algorithm outperforms the conventional algorithms in terms of accuracy, i.e., 98%. Finally, we present open issues and research challenges associated with smart healthcare technologies.

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“…A. Rokade and Singh [35] give a brief analysis of intelligent farming techniques which helps us to propose a sustainable intelligent farming model. A. Kadu and Singh [36] emphasize on distinguish data analysis for telemedicine systems which describes the uses of IoT and machine learning. Hassan et al [37][38][39] focus on the cognitive radio networks for improvement of capacity rate in 5G.…”

Section: Literature Reviewmentioning

confidence: 99%

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

Rokade

Singh

Arora

et al. 2022

Computational and Mathematical Methods in Medicine

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In the farming industry, the Internet of Things (IoT) is crucial for boosting utility. Innovative agriculture practices and medical informatics have the potential to increase crop yield while using the same amount of input. Individuals can benefit from the Internet of Things in various ways. The intelligent farms require the creation of an IoT-based infrastructure based on sensors, actuators, embedded systems, and a network connection. The agriculture sector will gain new advantages from machine learning and IoT data analytics in terms of improving crop output quantity and quality to fulfill rising food demand. This paper described an intelligent medical informatics farming system with predictive data analytics on sensing parameters, utilizing a supervised machine learning approach in an intelligent agricultural system. The four essential components of the proposed approach are the cloud layer, fog layer, edge layer, and sensor layer. The primary goal is to enhance production and provide organic farming by adjusting farming conditions as per plant needs that are considered in experimentation. The use of machine learning on acquired sensor data from a prototype embedded model is investigated for regulating the actuators in the system. Then, an analytics and decision-making system was built at the fog layer, employing two supervised machine learning approaches including classification and regression algorithms using a support vector machine (SVM) and artificial neural network (ANN) for effective computation over the cloud layer. The experimental results are evaluated and analyzed in MATLAB software, and it is found that the classification accuracy using SVM is much better as compared to ANN and other state of art methods.

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Comparative Analysis of e-Health Care Telemedicine System Based on Internet of Medical Things and Artificial Intelligence

Cited by 19 publications

References 51 publications

An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications

An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications

A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

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