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Detecting Alzheimer’s Disease Using Machine Learning Methods

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Body Area Networks. Smart IoT and Big Data for Intelligent Health Management (BODYNETS 2021)

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Abstract

As the world is experiencing population growth, the portion of the older people, aged 65 and above, is also growing at a faster rate. As a result, the dementia with Alzheimer’s disease is expected to increase rapidly in the next few years. Currently, healthcare systems require an accurate detection of the disease for its treatment and prevention. Therefore, it has become essential to develop a framework for early detection of Alzheimer’s disease to avoid complications. To this end, a novel framework, based on machine-learning (ML) and deep-learning (DL) methods, is proposed to detect Alzheimer’s disease. In particular, the performance of different ML and DL algorithms has been evaluated against their detection accuracy. The experimental results state that bidirectional long short-term memory (BiLSTM) outperforms the ML methods with a detection accuracy of 91.28%. Furthermore, the comparison with the state-of-the-art indicates the superiority of the our framework over the other proposed approaches in the literature.

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Acknowledgement

This work is supported in part by the Ajman University Internal Research Grant.

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Authors and Affiliations

  1. James Watt School of Engineering, University of Glasgow, Glasgow, UK

    Kia Dashtipour, William Taylor, Shuja Ansari, Muhammad Ali Imran & Qammer Abbasi

  2. School of Engineering and Physical Science, Heriot-Watt University, Edinburgh, EH144AS, UK

    Adnan Zahid

  3. School of Computing, Edinburgh Napier University, Edinburgh, UK

    Kia Dashtipour, Mandar Gogate & Jawad Ahmad

  4. Faculty of Engineering and IT, Ajman University, Ajman, 346, UAE

    Khaled Assaleh & Kamran Arshad

  5. Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, UAE

    Muhammad Ali Imran

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  1. Kia Dashtipour
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Correspondence to Kia Dashtipour .

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Editors and Affiliations

  1. University of Glasgow, Glasgow, UK

    Masood Ur Rehman

  2. University of Glasgow, Glasgow, UK

    Ahmed Zoha

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Dashtipour, K. et al. (2022). Detecting Alzheimer’s Disease Using Machine Learning Methods. In: Ur Rehman, M., Zoha, A. (eds) Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-030-95593-9_8

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  • DOI: https://doi.org/10.1007/978-3-030-95593-9_8

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