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Attaining an IoMT-based health monitoring and prediction: a hybrid hierarchical deep learning model and metaheuristic algorithm

  • S.I.: Intelligent Systems in Biomedical and Healthcare Informatics
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Abstract

Internet of Medical Things (IoMT) visualizes a network of medical devices and society adopting wireless communications to enable interchange of healthcare data. IoMT is utilized to gather real-time data from medical equipment and sensors. This enables possibility for continuous health monitoring and prediction. There is concern related to potential privacy and safety hazards connected with the group and transmission of sensitive health data over the network. This study proposes a hybrid hierarchical deep learning (DL) model enhanced with features and a metaheuristic algorithm to achieve health monitoring and prediction based on IoMT. The information gained from the analysis helps to identify important features for prediction. The feature selection phase applies Self-regularized Quantum Coronavirus Optimization Algorithm (SQCOA) to prioritize important features for prediction. The prediction phase includes Optimized Long Short-Term Memory (OLSTM) and Hierarchical Convolutional Spiking Neural Network (HCSNN) for feature learning and performance prediction, respectively. The proposed model is simulated by adopting MATLAB. The model attains the highest accuracy of 98%.

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Acknowledgements

The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Groups Funding program grant code (NU/RG/SERC/12/9).

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, 522302, India

    Prashant Kumar Shukla

  2. Department of Networks and Communications Engineering, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia

    Ali Alqahtani

  3. Jindal Global Business School, O.P. Jindal Global University, Sonipat, 131029, India

    Ashish Dwivedi

  4. Department of Electrical Engineering, College of Engineering, King Faisal University, 31982, Al-Hofuf, Al-Ahsa, Saudi Arabia

    Nayef Alqahtani

  5. Computer Science and Engineering Department, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (Technological University of Madhya Pradesh), Bhopal, India

    Piyush Kumar Shukla

  6. Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, 21589, Jeddah, Saudi Arabia

    Abdulaziz A. Alsulami

  7. Department of Operations Research and Statistics, Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia

    Dragan Pamucar

  8. College of Engineering, Yuan Ze University, Taoyuan City, 320315, Taiwan

    Dragan Pamucar

  9. Department of Computer Science and Mathematics, Lebanese American University, Byblos, 1102 2801, Lebanon

    Dragan Pamucar

  10. Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11010, Belgrade, Serbia

    Vladimir Simic

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  1. Prashant Kumar Shukla
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  5. Piyush Kumar Shukla
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Correspondence to Dragan Pamucar.

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Shukla, P.K., Alqahtani, A., Dwivedi, A. et al. Attaining an IoMT-based health monitoring and prediction: a hybrid hierarchical deep learning model and metaheuristic algorithm. Neural Comput & Applic (2023). https://doi.org/10.1007/s00521-023-09293-3

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