Authors:
Upul Senanayake
1
;
Arcot Sowmya
1
;
Laughlin Dawes
2
;
Nicole A. Kochan
1
;
Wei Wen
1
and
Perminder Sachdev
1
Affiliations:
1
UNSW, Australia
;
2
Prince of Wales Hospital, Australia
Keyword(s):
Alzheimer’s Disease, Mild Cognitive Impairment, Deep Learning, Neuropsychological Features.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Bioinformatics and Systems Biology
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Classification
;
Computational Intelligence
;
Ensemble Methods
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Imaging
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Software Engineering
;
Theory and Methods
Abstract:
Timely intervention in individuals at risk of dementia is often emphasized, and Mild Cognitive Impairment
(MCI) is considered to be an effective precursor to Alzheimers disease (AD), which can be used as an intervention
criterion. This paper attempts to use deep learning techniques to recognise MCI in the elderly. Deep
learning has recently come to attention with its superior expressive power and performance over conventional
machine learning algorithms. The current study uses variations of auto-encoders trained on neuropsychological
test scores to discriminate between cognitively normal individuals and those with MCI in a cohort of
community dwelling individuals aged 70-90 years. The performance of the auto-encoder classifier is further
optimized by creating an ensemble of such classifiers, thereby improving the generalizability as well. In
addition to comparable results to those of conventional machine learning algorithms, the auto-encoder based
classifiers also eliminate the need
for separate feature extraction and selection while also allowing seamless
integration of features from multiple modalities.
(More)