A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer’s Disease

Sensors

Orawit Thinnukool¹  Usman Tariq²  Seifedine Kadry³  Arnab Majumdar⁴  Anza Aqeel⁵  Ali Hassan⁵  Saad Rehman⁶  Muhammad Attique Khan⁶ 

[1] College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand;College of Computer Engineering and Science, Prince Sattam Bin Abdulaziz University, Al-Kharaj 16242, Saudi Arabia;Department of Applied Data Science, Noroff University College, 4608 Kristiansand, Norway;Department of Civil Engineering, Imperial College London, London SW7 2AZ, UK;Department of Computer & Software Engineering, CEME, NUST, Islamabad 44800, Pakistan;Department of Computer Engineering, HITEC University, Taxila 47080, Pakistan;

关键词: Alzheimer’s;    long short-term memory;    artificial neural network;    machine learning;   

DOI  :  10.3390/s22041475

来源: DOAJ

【 摘 要 】

The early prediction of Alzheimer’s disease (AD) can be vital for the endurance of patients and establishes as an accommodating and facilitative factor for specialists. The proposed work presents a robotized predictive structure, dependent on machine learning (ML) methods for the forecast of AD. Neuropsychological measures (NM) and magnetic resonance imaging (MRI) biomarkers are deduced and passed on to a recurrent neural network (RNN). In the RNN, we have used long short-term memory (LSTM), and the proposed model will predict the biomarkers (feature vectors) of patients after 6, 12, 21 18, 24, and 36 months. These predicted biomarkers will go through fully connected neural network layers. The NN layers will then predict whether these RNN-predicted biomarkers belong to an AD patient or a patient with a mild cognitive impairment (MCI). The developed methodology has been tried on an openly available informational dataset (ADNI) and accomplished an accuracy of 88.24%, which is superior to the next-best available algorithms.

【 授权许可】

Unknown