Detecting Alzheimer’s Disease Using Machine Learning Methods

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationBody Area Networks. Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2021
EditorsMasood Ur Rehman, Ahmed Zoha
PublisherSpringer
Pages89-100
Number of pages12
ISBN (Electronic)9783030955939
ISBN (Print)9783030955922
DOIs
Publication statusPublished - 11 Feb 2022
Event16th EAI International Conference on Body Area Networks 2021 - Virtual, Online
Duration: 25 Dec 202126 Dec 2021

Publication series

NameLecture Notes of the Institute for Computer Sciences
Volume420
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X

Conference

Conference16th EAI International Conference on Body Area Networks 2021
Abbreviated titleBODYNETS 2021
CityVirtual, Online
Period25/12/2126/12/21

Keywords

  • Deep learning
  • Detecting Alzheimer
  • Machine learning

ASJC Scopus subject areas

  • Computer Networks and Communications

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