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
N1 - Funding Information:
This work is supported in part by the Ajman University Internal Research Grant.
Funding Information:
This work is supported in part by the Ajman University Internal
Publisher Copyright:
© 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2022/2/11
Y1 - 2022/2/11
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 - http://www.scopus.com/inward/record.url?scp=85125249490&partnerID=8YFLogxK
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
SP - 89
EP - 100
BT - Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2021
A2 - Ur Rehman, Masood
A2 - Zoha, Ahmed
PB - Springer
T2 - 16th EAI International Conference on Body Area Networks 2021
Y2 - 25 December 2021 through 26 December 2021
ER -