Volume 8, Issue 3 (12-2021)
jhbmi 2021, 8(3): 326-337 |
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Golkar A, Malekhosseini R, RahimiZadeh K, Yazdani A, Beheshti A. Development of a Model for Predicting Heart Attack Based on Fog Computing. jhbmi 2021; 8 (3) :326-337
URL: http://jhbmi.ir/article-1-640-en.html
URL: http://jhbmi.ir/article-1-640-en.html
Ph.D. in Computer Engineering, Assistant Professor, Department of Electrical and Computer Engineering, School of Engineering, Yasouj University, Yasouj, Iran
Abstract: (2021 Views)
Introduction: Various studies have demonstrated the benefits of using distributed fog computing for the Internet of Things (IoT). Fog computing has brought cloud computing capabilities such as computing, storage, and processing closer to IoT nodes. The new model of fog and edge computing, compared to cloud computing, provides less latency for data processing by bringing resources closer to users. This is essential for delay-sensitive applications such as remote healthcare and provides more reliable services. In this study, a fog-based system was proposed to monitor the condition of heart patients.
Method: This study was a developmental-applied one. A set of data relevant to coronary heart patients available in the machine learning data repository of the University of California Irvine was used for evaluation. In this system, each of the heart patient's symptoms is evaluated based on the normal range in the fog layer and the status of the patient is determined. In this layer, requests are prioritized based on the number of symptoms that are out of the normal range. The efficiency of the proposed system was evaluated in terms of network usage time, latency, and response time.
Results: The system presented in this study led to the improvement of network usage time by 23.77%, reduction of latency by 23.71%, and enhancement of response time by 32.95%.
Conclusion: Using the priority queue to prioritize requests at the fog layer reduces the response time to emergency requests.
Method: This study was a developmental-applied one. A set of data relevant to coronary heart patients available in the machine learning data repository of the University of California Irvine was used for evaluation. In this system, each of the heart patient's symptoms is evaluated based on the normal range in the fog layer and the status of the patient is determined. In this layer, requests are prioritized based on the number of symptoms that are out of the normal range. The efficiency of the proposed system was evaluated in terms of network usage time, latency, and response time.
Results: The system presented in this study led to the improvement of network usage time by 23.77%, reduction of latency by 23.71%, and enhancement of response time by 32.95%.
Conclusion: Using the priority queue to prioritize requests at the fog layer reduces the response time to emergency requests.
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