Imperial College London
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ProfessorArnabMajumdar

Faculty of Engineering, Department of Civil and Environmental Engineering

Professor of Transport Risk and Safety
 
 
 
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Contact

 

+44 (0)20 7594 6037a.majumdar

 
 
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Assistant

 

Ms Maya Mistry +44 (0)20 7594 6100

 
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Location

 

604Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Aqeel:2022:10.3390/s22041475,
author = {Aqeel, A and Hassan, A and Khan, MA and Rehman, S and Tariq, U and Kadry, S and Majumdar, A and Thinnukool, O},
doi = {10.3390/s22041475},
journal = {Sensors (Basel)},
title = {A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer's Disease.},
url = {http://dx.doi.org/10.3390/s22041475},
volume = {22},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Aqeel,A
AU  - Hassan,A
AU  - Khan,MA
AU  - Rehman,S
AU  - Tariq,U
AU  - Kadry,S
AU  - Majumdar,A
AU  - Thinnukool,O
DO  - 10.3390/s22041475
PY  - 2022///
TI  - A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer's Disease.
T2  - Sensors (Basel)
UR  - http://dx.doi.org/10.3390/s22041475
UR  - https://www.ncbi.nlm.nih.gov/pubmed/35214375
VL  - 22
ER  -
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