Research Article
Detecting Alzheimer’s Disease Using Machine Learning Methods
@INPROCEEDINGS{10.1007/978-3-030-95593-9_8, 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 Muhammad Ali Imran and Qammer Abbasi}, title={Detecting Alzheimer’s Disease Using Machine Learning Methods}, proceedings={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}, proceedings_a={BODYNETS}, year={2022}, month={2}, keywords={Machine learning Deep learning Detecting Alzheimer}, doi={10.1007/978-3-030-95593-9_8} }- Kia Dashtipour
William Taylor
Shuja Ansari
Adnan Zahid
Mandar Gogate
Jawad Ahmad
Khaled Assaleh
Kamran Arshad
Muhammad Ali Imran
Qammer Abbasi
Year: 2022
Detecting Alzheimer’s Disease Using Machine Learning Methods
BODYNETS
Springer
DOI: 10.1007/978-3-030-95593-9_8
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.
