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Disease Diagnosis System for IoT-Based Wearable Body Sensors with Machine Learning Algorithm

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Hybrid Artificial Intelligence and IoT in Healthcare

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 209))

Abstract

In recent years, the continuous growth in global infectious disease coupled with population growth and the associated increase in expectancy lead to the search for new ways of making the most use of limited resources. Automated disease monitoring, diagnosis, prediction, and treatment of patients are not only for fast data but also to get reliable service at reduced cost and accurate results from medical experts. The combined wearable devices and Internet of Things (IoT) have been reformed the medical system. This has minimized the response time in monitoring, diagnosis, prediction, and treatment by thrives toward the omnipresence of the healthcare services. However, the integration and design of IoT-based wearable sensors present many challenges especially in the areas of data exchange, monitoring, and diagnosing of patients. Therefore, the chapter proposes a framework for IoT-WBN-based with a machine learning algorithm (ML). The data collected from different wearable sensors like body temperature, glucose sensors, heartbeat sensors, and chest were transmitted through IoT devices to the integrated cloud database. To select the most useful features from the capture data, the ML was used, and the sensor signal is analyzed using ML for diagnosis of patient data. The proposed framework can be widely used in a remote area to monitor and diagnose patient health conditions to reduce, and eliminate medical faults, reduce healthcare costs, minimize pressure on medical experts, increase productivity, and enhancing patient satisfaction.

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Authors and Affiliations

  1. Department of Computer Science, University of Ilorin, Ilorin, Nigeria

    Joseph Bamidele Awotunde

  2. Department of Mathematical Science, Olabisi Onabanjo University, Ago-Iwoye, Nigeria

    Sakinat Oluwabukonla Folorunso

  3. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology (SMIT), Sikkim Manipal University (SMU), Majitar, Sikkim, 737136, India

    Akash Kumar Bhoi

  4. Department of Computer Science, Federal Polytechnic, Nasarawa, Nigeria

    Paul Olujide Adebayo

  5. Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006, South Korea

    Muhammad Fazal Ijaz

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  1. Joseph Bamidele Awotunde
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  2. Sakinat Oluwabukonla Folorunso
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  3. Akash Kumar Bhoi
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  4. Paul Olujide Adebayo
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  5. Muhammad Fazal Ijaz
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Correspondence to Joseph Bamidele Awotunde .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology (SMIT), Sikkim Manipal University, Majitar, Sikkim, India

    Akash Kumar Bhoi

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University, Bhubaneswar, Odisha, India

    Pradeep Kumar Mallick

  3. Department of Electronics and Communication Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

    Mihir Narayana Mohanty

  4. Graduate Program on Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Brazil

    Victor Hugo C. de Albuquerque

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Awotunde, J., Folorunso, S.O., Bhoi, A.K., Adebayo, P.O., Ijaz, M.F. (2021). Disease Diagnosis System for IoT-Based Wearable Body Sensors with Machine Learning Algorithm. In: Kumar Bhoi, A., Mallick, P.K., Narayana Mohanty, M., Albuquerque, V.H.C.d. (eds) Hybrid Artificial Intelligence and IoT in Healthcare. Intelligent Systems Reference Library, vol 209. Springer, Singapore. https://doi.org/10.1007/978-981-16-2972-3_10

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