Detecting Alzheimer's disease using machine learning methods - Archive ouverte HAL
Communication Dans Un Congrès Année : 2021

Detecting Alzheimer's disease using machine learning methods

Kia Dashtipour
  • Fonction : Auteur
  • PersonId : 1097909
William Taylor
Shuja Ansari
  • Fonction : Auteur
Adnan Zahid
  • Fonction : Auteur
Mandar Gogate
  • Fonction : Auteur
Jawad Ahmad
  • Fonction : Auteur
Khaled Assaleh
  • Fonction : Auteur
Kamran Arshad
  • Fonction : Auteur
Muhammad Ali Imran
  • Fonction : Auteur
Qammer Abbai
  • Fonction : Auteur

Résumé

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 stateof-the-art indicates the superiority of the our framework over the other proposed approaches in the literature.
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Dates et versions

hal-03381752 , version 1 (17-10-2021)

Identifiants

  • HAL Id : hal-03381752 , version 1

Citer

Kia Dashtipour, William Taylor, Shuja Ansari, Adnan Zahid, Mandar Gogate, et al.. Detecting Alzheimer's disease using machine learning methods. EAI, Oct 2021, Glasgow, United Kingdom. ⟨hal-03381752⟩
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