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

doi:10.1007/978-3-030-95593-9_8

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

University of Glasgow
Edinburgh Napier University
Heriot-Watt University
Ajman University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

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 language	English
Title of host publication	Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings
Editors	Masood Ur Rehman, Ahmed Zoha
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	89-100
Number of pages	12
ISBN (Print)	9783030955922
DOIs	https://doi.org/10.1007/978-3-030-95593-9_8
State	Published - 2022
Event	16th EAI International Conference on Body Area Networks, BODYNETS 2021 - Virtual, Online Duration: 25 Dec 2021 → 26 Dec 2021

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	420 LNICST
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	16th EAI International Conference on Body Area Networks, BODYNETS 2021
City	Virtual, Online
Period	25/12/21 → 26/12/21

Keywords

Deep learning
Detecting Alzheimer
Machine learning

Access to Document

10.1007/978-3-030-95593-9_8

Cite this

Dashtipour, K., Taylor, W., Ansari, S., Zahid, A., Gogate, M., Ahmad, J., Assaleh, K., Arshad, K., Imran, M. A., & Abbasi, Q. (2022). Detecting Alzheimer’s Disease Using Machine Learning Methods. In M. Ur Rehman, & A. Zoha (Eds.), Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings (pp. 89-100). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 420 LNICST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-95593-9_8

Dashtipour, Kia ; Taylor, William ; Ansari, Shuja et al. / Detecting Alzheimer’s Disease Using Machine Learning Methods. Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings. editor / Masood Ur Rehman ; Ahmed Zoha. Springer Science and Business Media Deutschland GmbH, 2022. pp. 89-100 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{7deedc01ffa14978a3538e5978e617e1,

title = "Detecting Alzheimer{\textquoteright}s Disease Using Machine Learning Methods",

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{\textquoteright}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{\textquoteright}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{\textquoteright}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.",

keywords = "Deep learning, Detecting Alzheimer, Machine learning",

author = "Kia Dashtipour and William Taylor and Shuja Ansari and Adnan Zahid and Mandar Gogate and Jawad Ahmad and Khaled Assaleh and Kamran Arshad and Imran, \{Muhammad Ali\} and Qammer Abbasi",

note = "Publisher Copyright: {\textcopyright} 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 16th EAI International Conference on Body Area Networks, BODYNETS 2021 ; Conference date: 25-12-2021 Through 26-12-2021",

year = "2022",

doi = "10.1007/978-3-030-95593-9\_8",

language = "English",

isbn = "9783030955922",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "89--100",

editor = "\{Ur Rehman\}, Masood and Ahmed Zoha",

booktitle = "Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings",

address = "Germany",

}

Dashtipour, K, Taylor, W, Ansari, S, Zahid, A, Gogate, M, Ahmad, J, Assaleh, K , Arshad, K, Imran, MA & Abbasi, Q 2022, Detecting Alzheimer’s Disease Using Machine Learning Methods. in M Ur Rehman & A Zoha (eds), Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 420 LNICST, Springer Science and Business Media Deutschland GmbH, pp. 89-100, 16th EAI International Conference on Body Area Networks, BODYNETS 2021, Virtual, Online, 25/12/21. https://doi.org/10.1007/978-3-030-95593-9_8

Detecting Alzheimer’s Disease Using Machine Learning Methods. / Dashtipour, Kia; Taylor, William; Ansari, Shuja et al.
Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings. ed. / Masood Ur Rehman; Ahmed Zoha. Springer Science and Business Media Deutschland GmbH, 2022. p. 89-100 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 420 LNICST).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detecting Alzheimer’s Disease Using Machine Learning Methods

AU - Dashtipour, Kia

AU - Taylor, William

AU - Ansari, Shuja

AU - Zahid, Adnan

AU - Gogate, Mandar

AU - Ahmad, Jawad

AU - Assaleh, Khaled

AU - Arshad, Kamran

AU - Imran, Muhammad Ali

AU - Abbasi, Qammer

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Deep learning

KW - Detecting Alzheimer

KW - Machine learning

UR - https://www.scopus.com/pages/publications/85125249490

U2 - 10.1007/978-3-030-95593-9_8

DO - 10.1007/978-3-030-95593-9_8

M3 - Conference contribution

AN - SCOPUS:85125249490

SN - 9783030955922

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 89

EP - 100

BT - Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings

A2 - Ur Rehman, Masood

A2 - Zoha, Ahmed

PB - Springer Science and Business Media Deutschland GmbH

T2 - 16th EAI International Conference on Body Area Networks, BODYNETS 2021

Y2 - 25 December 2021 through 26 December 2021

ER -

Dashtipour K, Taylor W, Ansari S, Zahid A, Gogate M, Ahmad J et al. Detecting Alzheimer’s Disease Using Machine Learning Methods. In Ur Rehman M, Zoha A, editors, Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 89-100. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-95593-9_8

Detecting Alzheimer’s Disease Using Machine Learning Methods

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this