Heart Disease Prediction using Fog Computing based Wireless Body Sensor Networks (WSNs)

Shakya, Subarna; Joby, P. P.

doi:10.36548/jsws.2021.1.006

Cited by 33 publications

(4 citation statements)

References 11 publications

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“…The data collector is set up, and the calculation is carried out using a decentralized system using a fog computing environment. After integrating the nodes' results using the fog computing database, a final output is generated [17].…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we evaluate the efficacy of our suggested approach using the performance indicator indicated before. Our proposed technique is performed and also matched with other standard techniques (MANAGE-HF [50], GA [51], ML [52], BSNs [17], MSABA). Heart failure patients experience a range of physical and psychological symptoms, including dyspnea, fatigue, edema, trouble sleeping, sadness, and chest discomfort.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The suggested algorithm's efficacy is evaluated using these metrics. The outcomes were compared to those obtained using conventional methods such as Multiple Cardiac Sensors for the management of heart failure (MANAGE-HF) [50], genetic algorithms (GA) [51], machine learning approaches (ML) [52], and body sensor networks (BSNs) [17]. As per data collection, the datasets are gathered out of public health datasets.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Modified Self-Adaptive Bayesian Algorithm for Smart Heart Disease Prediction in IoT System

et al. 2022

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Heart disease (HD) has surpassed all other causes of death in recent years. Estimating one’s risk of developing heart disease is difficult, since it takes both specialized knowledge and practical experience. The collection of sensor information for the diagnosis and prognosis of cardiac disease is a recent application of Internet of Things (IoT) technology in healthcare organizations. Despite the efforts of many scientists, the diagnostic results for HD remain unreliable. To solve this problem, we offer an IoT platform that uses a Modified Self-Adaptive Bayesian algorithm (MSABA) to provide more precise assessments of HD. When the patient wears the smartwatch and pulse sensor device, it records vital signs, including electrocardiogram (ECG) and blood pressure, and sends the data to a computer. The MSABA is used to determine whether the sensor data that has been obtained is normal or abnormal. To retrieve the features, the kernel discriminant analysis (KDA) is used. By contrasting the suggested MSABA with existing models, we can summarize the system’s efficacy. Findings like accuracy, precision, recall, and F1 measures show that the suggested MSABA-based prediction system outperforms competing approaches. The suggested method demonstrates that the MSABA achieves the highest rate of accuracy compared to the existing classifiers for the largest possible amount of data.

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Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

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Modified Self-Adaptive Bayesian Algorithm for Smart Heart Disease Prediction in IoT System

et al. 2022

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“…The rejection frequency depends on the capacitance and inductance produced by the conductor portion of FSS. The proposed FSS is inspired by square loop FSS, 39–41 and the resonance frequency is given as Equation . The spacing decides the capacitance of FSS between Unit cells (2L g ) and the periodicity of unit cells.…”

Section: Antenna and Fss Designmentioning

confidence: 99%

Performance evaluation of compact FSS‐integrated flexible monopole antenna for body area communication applications

Sugumaran

Balasubramanian

Palaniswamy

2022

Int J Communication

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Summary This article presents a compact wearable antenna integrated with a novel frequency selective surface (FSS) made of textile materials. The antenna can be applied in wearable health monitoring applications aimed at the 2.45‐GHz Industrial Scientific and Medical (ISM) band. An array of 4 × 4 octagonal slot‐shaped FSS (106 × 106 mm2) superstrate enhances the performance of the modified Koch curve planar monopole antenna (44.85 × 39 mm2). The Jeans substrate prototype with the overall dimensions of 1.128λ0 × 1.128λ0 × 0.319λ0 covers the bandwidth of 2.45‐GHz ISM band from 2.24 to 2.76 GHz (S11 ≤ −10 dB). The band rejection and reflective nature of the proposed FSS suppress the back radiation and improves the antenna's gain by −20 dB and 6.3 dBi, respectively. The specific absorption rate (SAR) level is reduced to 0.445 W/kg from 24.995 W/kg with a higher reduction rate of 98.21%. Many literature reports produced higher gain and lower SAR but exhibited reduced efficiency when the antenna is placed near the human body. The wearable antenna's frequency response tends to vary under bending conditions for narrowband antennas. Wide‐band monopole antennas produce omnidirectional radiation pattern which tends to affect human body tissues. So stable polarization‐insensitive FSS is placed behind the antenna to enhance the antenna's gain and avoid back radiation. The proposed antenna exhibits stable performance under bending conditions with fractional bandwidth of 22.2%, gain of 8.83 dBi, and produces a better efficiency of 91.30% when the antenna is positioned in the vicinity with human body models. The experimental validation shows that the proposed antenna is a promising candidate for wireless body area networks.

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DIWGAN‐WBSN: A novel health monitoring approach for wireless body sensor networks

Jayasutha,

Hemamalini,

Sangeetha

et al. 2024

Int J Communication

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SummaryWireless body sensor network (WBSN) is essential for monitoring patients' health problems and offers a low‐cost option for various healthcare applications. In this manuscript, a Novel Health Monitoring Approach for WBSNs (DIWGAN‐WBSN) is proposed, which uses Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) optimized with War Strategy Optimization Algorithm (WSOA). After sensing the aforementioned attribute information, it is the responsibility of WBSN nodes to transfer the sensed data to the sink node. The Volcano Eruption Algorithm (VEA) is applied to select the optimum cluster heads in WBSN. The results from VEA are fed to the target node; it consists of DIWGAN to classify the health records and to portray the patient's health status. Generally, DIWGAN does not adopt any optimization methods for measuring the ideal parameters and guaranteeing accurate health monitoring and risk assessment. So the proposed WSOA is considered to enhance the DIWGAN. The proposed method is activated in MATLAB; its efficacy is estimated under performance metrics, like precision, specificity, accuracy, and energy utilization. The proposed approach attains 23.9%, 21.34%, and 51.09% higher accuracy; 21.45%, 13.94%, and 20.6% higher precision; 31.32%, 29.61%, and 11.03% higher specificity; and 20.9%, 19.87%, and 24.6% lower energy utilization for HD classification using the Cleveland database than the existing methods like back propagation neural network‐based risk detection in WBSN for health monitoring, random forest algorithm–based health monitoring in WBSN, and ensemble deep learning and feature fusion for health monitoring using WBSN methods, respectively.

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Heart Disease Prediction using Fog Computing based Wireless Body Sensor Networks (WSNs)

Cited by 33 publications

References 11 publications

Modified Self-Adaptive Bayesian Algorithm for Smart Heart Disease Prediction in IoT System

Modified Self-Adaptive Bayesian Algorithm for Smart Heart Disease Prediction in IoT System

Performance evaluation of compact FSS‐integrated flexible monopole antenna for body area communication applications

DIWGAN‐WBSN: A novel health monitoring approach for wireless body sensor networks

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