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Detecting Alzheimer’s Disease Using Machine Learning Methods

Dashtipour, Kia; Taylor, William; Ansari, Shuja; Zahid, Adnan; Gogate, Mandar; Ahmad, Jawad; Assaleh, Khaled; Arshad, Kamran; Ali Imran, Muhammad; Abbasi, Qammer


William Taylor

Shuja Ansari

Adnan Zahid

Khaled Assaleh

Kamran Arshad

Muhammad Ali Imran

Qammer Abbasi


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.

Presentation Conference Type Conference Paper (Published)
Conference Name 16th EAI International Conference, BODYNETS 2021
Start Date Oct 25, 2021
End Date Oct 26, 2021
Online Publication Date Feb 11, 2022
Publication Date 2022
Deposit Date Jun 23, 2022
Publisher Springer
Peer Reviewed Peer Reviewed
Pages 89-100
Series Title Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Series Number 420
Series ISSN 1867-822X
Book Title Body Area Networks. Smart IoT and Big Data for Intelligent Health Management 16th EAI International Conference, BODYNETS 2021, Virtual Event, October 25-26, 2021, Proceedings
ISBN 978-3-030-95592-2
Public URL