A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms

A, Ahila; Dahan, Fadl; Alroobaea, Roobaea; Alghamdi, Wael. Y.; Mohammed, Mustafa Khaja; Hajjej, Fahima; alsekait, Deema mohammed; Raahemifar, Kaamran

doi:10.3389/fphys.2023.1125952

Cited by 24 publications

(6 citation statements)

References 34 publications

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“…The paper [7] used seven ML classification algorithms to forecast nine deadly illnesses, including thyroid, diabetes, hepatitis, liver disordersand heart disease, dermatology and breast cancer.The paper [8] suggested method used the Cascaded Long Short Term Memory (CSO-CLSTM) illness diagnosis model, which was based on the CSO algorithm. The paper [9] used sensing devices to gather data from the patient's body at first. Following transmission via a gateway and WiFi, the information was stored in a cloud repository for the Internet of Medical Things (IoMT).…”

Section: Related Workmentioning

confidence: 99%

IoT-Cloud-based Smart Healthcare Monitoring System based on Seahorse Optimization-driven Mutated CNN

Preetha P.

2024

jes

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Healthcare is undergoing a transformation due to predictive analytics, which offers clinical decision support in real-time, facilitating effective, tailored therapy. By lowering unfavorable events and raising the standard of care, this approach supports patients at every step of diagnosis, prognosis and therapy. Predictive algorithms driven by artificial intelligence (AI), machine learning (ML) and Internet-of-Things (IoT) handle enormous amounts of data, with cardiovascular illnesses as the primary cause of death worldwide. IoT devices make sophisticated deep learning (DL) analytics and remote patient monitoring possible. This paper presents a novel 5-stage disease prediction model as an IoT-cloud-based smart healthcare management framework, constructed using a seahorse optimization-driven mutated convolutional neural network (SHO-MuCNN). In stage-1, data is gathered from a variety of sources through data collection. The stage-2 of data pre-processing involves the use of standardization to get the data acceptable for additional processing. Using a customized principal component analysis (CPCA), the set of characteristics is determined in stage-3. Next, the stage-4 diseases are predicted by the suggested SHO-MuCNN methodology. According to the prediction results, physicians are educating patients in stage-5 on exactly what measures can be taken to reduce the risk. The smart healthcare monitoring measures for accuracy (98.56%), precision (96.69%), F1-score (97.86%), Recall (94.59%)and AUC (95.02), were employed in the models' performance. In addition, this study utilizes the SPSS and t-test for statistical analysis. The experiment's findings indicate that the SHO-MuCNN strategy outperformed the remaining techniques in an intelligent healthcare monitoring system.

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Section: Related Workmentioning

confidence: 99%

IoT-Cloud-based Smart Healthcare Monitoring System based on Seahorse Optimization-driven Mutated CNN

Preetha P.

2024

jes

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“…Also, Dahan et al 46 presented a smart IoMT architecture designed for an E‐healthcare patient monitoring system, incorporating AI algorithms. Their system aimed to enhance healthcare monitoring by leveraging AI techniques for data analysis.…”

Section: Nature‐inspired Algorithmsmentioning

confidence: 99%

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

Amiri,

Heidari,

Zavvar

et al. 2024

Trans Emerging Tel Tech

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Nature‐inspired algorithms revolve around the intersection of nature‐inspired algorithms and the IoT within the healthcare domain. This domain addresses the emerging trends and potential synergies between nature‐inspired computational approaches and IoT technologies for advancing healthcare services. Our research aims to fill gaps in addressing algorithmic integration challenges, real‐world implementation issues, and the efficacy of nature‐inspired algorithms in IoT‐based healthcare. We provide insights into the practical aspects and limitations of such applications through a systematic literature review. Specifically, we address the need for a comprehensive understanding of the applications of nature‐inspired algorithms in IoT‐based healthcare, identifying gaps such as the lack of standardized evaluation metrics and studies on integration challenges and security considerations. By bridging these gaps, our paper offers insights and directions for future research in this domain, exploring the diverse landscape of nature‐inspired algorithms in healthcare. Our chosen methodology is a Systematic Literature Review (SLR) to investigate related papers rigorously. Categorizing these algorithms into groups such as genetic algorithms, particle swarm optimization, cuckoo algorithms, ant colony optimization, other approaches, and hybrid methods, we employ meticulous classification based on critical criteria. MATLAB emerges as the predominant programming language, constituting 37.9% of cases, showcasing a prevalent choice among researchers. Our evaluation emphasizes adaptability as the paramount parameter, accounting for 18.4% of considerations. By shedding light on attributes, limitations, and potential directions for future research and development, this review aims to contribute to a comprehensive understanding of nature‐inspired algorithms in the dynamic landscape of IoT‐based healthcare services.

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“…Artificial intelligence algorithms were employed in [15] for monitoring healthcare patients. Also to focus on the optimality issues, a multi-objective cuckoo search algorithm along with linear discriminant analysis was presented in [16].…”

Section: Critical Review Of Literature and Identification Of Research...mentioning

confidence: 99%

Generative Deep Belief Differential Lotka Volterra-Based Data Transmission in Wsn for Real-Time Health Care Monitoring

Sachin,

Sarvagya

2024

Preprint

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Wireless Sensor Networks (WSNs) have considerable awareness owing to their extensive scope of applications, to name a few being, natural disaster prohibition, health care monitoring video surveillance and so on. Moreover, the energy consumed during communication is a substantial quantity of energy loss and the routing protocol is the most suitable means to address this issue by fine-tuning weights to conserve the energy. Therefore the main issue experienced by WSN is the routing and data transmission and hence distinct points of reference need to be addressed when designing the routing protocol for real time healthcare monitoring. Motivated by these issues, in this work a method called, Probabilistic Generative Deep Belief and Differential Game Lotka Volterra (PGDB-DGLV) based data transmission in WSN for real-time health care monitoring is proposed. The PGDB-DGLV method is split into two sections, namely, optimal route discovery and robust data transmission. To measure route stability Balanced Centrality and Probabilistic Generative Deep Belief Network algorithm is designed. In the proposed model, a fine-tuned adjacency matrix employing the Stable Balanced Centrality function is defined to determine the next hop based on certain parameters like, hop count, residual energy and link quality. These parameters are salient assessments in adjacent node selections with which routing can be performed. Also, the initialization of the adjacency matrix and its corresponding fine-tuning is performed via the Probabilistic Generative Deep Belief Network. Second, to minimize the transmission speed and therefore reduce packet loss significantly, two kinds of game models, i.e., cooperative and cooperative models are constructed. Finally, the Lotka Volterra function is applied to ensure robust data transmission. At last, the performance of the proposed PGDB-DGLV method is evaluated using different evaluation metrics they are energy consumption, throughput transmission speed and packet loss rate. Finally, the evaluated results are compared with the prevailing data transmission methods. Among these methods, the proposed PGDB-DGLV method has achieved a better transmission speed with minimum energy consumption and maximum throughput considerably.

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A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms

Cited by 24 publications

References 34 publications

IoT-Cloud-based Smart Healthcare Monitoring System based on Seahorse Optimization-driven Mutated CNN

IoT-Cloud-based Smart Healthcare Monitoring System based on Seahorse Optimization-driven Mutated CNN

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

Generative Deep Belief Differential Lotka Volterra-Based Data Transmission in Wsn for Real-Time Health Care Monitoring

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