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

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.

Citation

Dashtipour, K., Taylor, W., Ansari, S., Zahid, A., Gogate, M., Ahmad, J., Assaleh, K., Arshad, K., Ali Imran, M., & Abbasi, Q. (2021, October). Detecting Alzheimer’s Disease Using Machine Learning Methods. Presented at 16th EAI International Conference, BODYNETS 2021, Online

• 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
• DOI: https://doi.org/10.1007/978-3-030-95593-9_8
• Public URL: http://researchrepository.napier.ac.uk/Output/2867794