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A Body Area Network Approach for Stroke-Related Disease Diagnosis Using Artificial Intelligence with Deep Learning Techniques

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Advances in Computing and Data Sciences (ICACDS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1613))

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Abstract

Stroke is the second largest disease after heart disease that leads to death. Stroke-related diseases need immediate attention from health care experts. With rapid growth and advancements in the field of medical technologies across the world, there is a huge demand for the latest wireless communication technology, especially for the continuous monitoring of patients. Body area network is one among the promising technology which uses special-purpose sensor networks with design principles to function independently across various platforms to connect several medical sensors and related applications. The Body area network was applied in most of the medical and its related applications starting from basic patient monitoring systems to advanced critical disease diagnosis applications which provides a high degree of health care services not only to patients but also to health care professionals. The main objective of this research work was to propose a new medical approach for earlier disease diagnoses for stroke and its related disease that require immediate treatments. This work Integrates the body area network with artificial intelligence techniques which enables health care workers to speed up the diagnosis process that needs immediate attention. The experimental results show that the proposed approach obtained better results with an accuracy of 88.47% when compared to other existing models and also identified that this model is best suited for disease diagnosis, especially for stroke-related issues.

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Authors and Affiliations

  1. Department of Computer Applications, Graphic Era Deemed to be University, Dehradun, India

    M. Anand Kumar

  2. Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India

    A. Suresh Kumar

Authors
  1. M. Anand Kumar
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  2. A. Suresh Kumar
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Correspondence to M. Anand Kumar .

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Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, India

    P. K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

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Kumar, M.A., Kumar, A.S. (2022). A Body Area Network Approach for Stroke-Related Disease Diagnosis Using Artificial Intelligence with Deep Learning Techniques. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1613. Springer, Cham. https://doi.org/10.1007/978-3-031-12638-3_21

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  • DOI: https://doi.org/10.1007/978-3-031-12638-3_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12637-6

  • Online ISBN: 978-3-031-12638-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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