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An Enhanced Internet of Things Enabled Type-2 Fuzzy Logic for Healthcare System Applications

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Recent Trends on Type-2 Fuzzy Logic Systems: Theory, Methodology and Applications

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 425))

Abstract

Due to advancements in information and communication technology, the Internet of Things has gained popularity in a variety of academic fields. In IoT-based healthcare systems, numerous wearable sensors are employed to collect various data from patients. The healthcare system has been challenged by the increase in the number of people living with chronic and infectious diseases. There are several existing IoT-based healthcare systems and ontology-based methods to judiciously diagnose, and monitor patients with chronic diseases in real-time and for a very long term. This was done to drastically minimize the vast manual labor in healthcare monitoring and recommendation systems. The current monitoring and recommendation systems generally utilised Type-1 Fuzzy Logic (T1FL) or ontology that is unsuitable owing to uncertainty and inconsistency in the processing, and analysis of observed data. Due to the expansion of risk and unpredictable factors in chronic and infectious patients such as diabetes, heart attacks, and COVID-19, these healthcare systems cannot be utilized to collect thorough physiological data about patients. Furthermore, utilizing the current T1FL ontology-based method to extract the ideal membership value of risk factors becomes challenging and problematic, resulting in unsatisfactory outcomes. Therefore, this chapter discusses the applicability of IoT-based enabled Type-2 Fuzzy Logic (T2FL) in the healthcare system, and the challenges and prospects of their applications were also reviewed. The chapter proposes an IoT-based enabled T2FL system for monitoring patients with diabetes by extracting the physiological factors from patients’ bodies. The wearable sensors were used to capture the physiological factors of the patients, and the data capture was used for the monitoring of patients. The results from the experiment reveal that the model is very efficient and effective for diabetes patient monitoring, using patient risk factors.

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Authors and Affiliations

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

    Joseph Bamidele Awotunde

  2. Department of Computer Science, Federal University, Oye-Ekiti, Nigeria

    Olaiya Folorunsho

  3. Department of Computer Science, Alhikmah University, Ilorin, Nigeria

    Isah Olawale Mustapha

  4. Department of Computer Science, Tai Solarin University of Education, Ijagun, Nigeria

    Olayinka Olufunmilayo Olusanya

  5. Kwara State Polytechnic, Ilorin, Nigeria

    Mulikat Bola Akanbi

  6. Department of Computer Science, Landmark University, Omu Aran, Nigeria

    Kazeem Moses Abiodun

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  1. Joseph Bamidele Awotunde
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  1. Research Chair of Graduate Studies, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Department of Electronics and Communication Engineering, National Institute of Technology, Patna, India

    Anupam Kumar

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Awotunde, J.B., Folorunsho, O., Mustapha, I.O., Olusanya, O.O., Akanbi, M.B., Abiodun, K.M. (2023). An Enhanced Internet of Things Enabled Type-2 Fuzzy Logic for Healthcare System Applications. In: Castillo, O., Kumar, A. (eds) Recent Trends on Type-2 Fuzzy Logic Systems: Theory, Methodology and Applications. Studies in Fuzziness and Soft Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-031-26332-3_9

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