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Priority Detector and Classifier Techniques Based on ML for the IoMT

Rayan A. Alsemmeari1,*, Mohamed Yehia Dahab2, Badraddin Alturki1, Abdulaziz A. Alsulami3

1 Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
2 Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
3 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

* Corresponding Author: Rayan A. Alsemmeari. Email: email

Computers, Materials & Continua 2023, 76(2), 1853-1870. https://doi.org/10.32604/cmc.2023.038589

Abstract

Emerging telemedicine trends, such as the Internet of Medical Things (IoMT), facilitate regular and efficient interactions between medical devices and computing devices. The importance of IoMT comes from the need to continuously monitor patients’ health conditions in real-time during normal daily activities, which is realized with the help of various wearable devices and sensors. One major health problem is workplace stress, which can lead to cardiovascular disease or psychiatric disorders. Therefore, real-time monitoring of employees’ stress in the workplace is essential. Stress levels and the source of stress could be detected early in the fog layer so that the negative consequences can be mitigated sooner. However, overwhelming the fog layer with extensive data will increase the load on fog nodes, leading to computational challenges. This study aims to reduce fog computation by proposing machine learning (ML) models with two phases. The first phase of the ML model assesses the priority of the situation based on the stress level. In the second phase, a classifier determines the cause of stress, which was either interruptions or time pressure while completing a task. This approach reduced the computation cost for the fog node, as only high-priority records were transferred to the fog. Low-priority records were forwarded to the cloud. Four ML approaches were compared in terms of accuracy and prediction speed: K-nearest neighbors (KNN), a support vector machine (SVM), a bagged tree (BT), and an artificial neural network (ANN). In our experiments, ANN performed best in both phases because it scored an F1 score of 99.97% and had the highest prediction speed compared with KNN, SVM, and BT.

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Cite This Article

APA Style
Alsemmeari, R.A., Dahab, M.Y., Alturki, B., Alsulami, A.A. (2023). Priority detector and classifier techniques based on ML for the iomt. Computers, Materials & Continua, 76(2), 1853-1870. https://doi.org/10.32604/cmc.2023.038589
Vancouver Style
Alsemmeari RA, Dahab MY, Alturki B, Alsulami AA. Priority detector and classifier techniques based on ML for the iomt. Comput Mater Contin. 2023;76(2):1853-1870 https://doi.org/10.32604/cmc.2023.038589
IEEE Style
R.A. Alsemmeari, M.Y. Dahab, B. Alturki, and A.A. Alsulami "Priority Detector and Classifier Techniques Based on ML for the IoMT," Comput. Mater. Contin., vol. 76, no. 2, pp. 1853-1870. 2023. https://doi.org/10.32604/cmc.2023.038589



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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