An intelligent approach for enhancing the Quality of Service in IoMT based on 5G

Noora Jamal Ali, Noor Amer Hamzah, Alaa Majeed Ali, Poh Soon JosephNg, Jamal Fadhil Tawfeq, Ahmed Dheyaa Radhi


The concept and growth of superior individualized healthcare technologies are influenced in significant ways by the emerging areas of “Artificial Intelligence (AI) and the Internet of Things (IoT)”. Most people use wearable devices for mHealth, hence there are many potential applications for the “Internet of Medical Things (IoMT)”. Only 5G can provide the necessary support for smart medical devices to perform many different types of demanding computing activities. Today, heart disease was the major mortality on a global scale. For patients who need a greater accurate diagnosis and treatment, the advancement of medical innovation has created new obstacles. Although many studies have focused on diagnosing cardiac disease, the findings are often inaccurate and fail to fulfill patients' expectations of quality of service (QoS). So, this paper introduces a novel “feed-forward Bi-directional long-short term memory (FF-Bi-LSTM) algorithm to predict heart disease more accurately with enhanced QoS in IoMT based on 5G”. Linear discriminant analysis (LDA) and min-max normalization are employed, respectively, for preprocessing and feature extraction. The efficacy of the suggested approach is measured using several different metrics, including accuracy, precision, recall, and f1-score. The proposed method is also compared to certain existing techniques. These results show that the suggested strategy outperforms existing strategies in terms of improving QoS.

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Copyright (c) 2023 Noora Jamal Ali, Noor Amer Hamzah, Alaa Majeed Ali, Poh Soon JosephNg, Jamal Fadhil Tawfeq, Ahmed Dheyaa Radhi

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ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License