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Machine learning techniques for the diagnosis of Alzheimer's disease: A review
Alzheimer's disease is an incurable neurodegenerative disease primarily affecting the
elderly population. Efficient automated techniques are needed for early diagnosis of …
elderly population. Efficient automated techniques are needed for early diagnosis of …
Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges
Human gait provides a way of locomotion by combined efforts of the brain, nerves, and
muscles. Conventionally, the human gait has been considered subjectively through visual …
muscles. Conventionally, the human gait has been considered subjectively through visual …
[HTML][HTML] Advancements in computer-assisted diagnosis of Alzheimer's disease: A comprehensive survey of neuroimaging methods and AI techniques for early …
Alzheimer's Disease (AD) is a brain disorder that causes the brain to shrink and eventually
causes brain cells to die. This neurological condition progressively hampers cognitive and …
causes brain cells to die. This neurological condition progressively hampers cognitive and …
Imaging and machine learning techniques for diagnosis of Alzheimer's disease
Alzheimer's disease (AD) is a common health problem in elderly people. There has been
considerable research toward the diagnosis and early detection of this disease in the past …
considerable research toward the diagnosis and early detection of this disease in the past …
Machine learning and similarity network approaches to support automatic classification of parkinson's diseases using accelerometer-based gait analysis
E Rastegari, S Azizian, H Ali - 2019 - scholarspace.manoa.hawaii.edu
Parkinson's Disease is a worldwide health problem, causing movement disorder and gait
deficiencies. Automatic noninvasive techniques for Parkinson's disease diagnosis is …
deficiencies. Automatic noninvasive techniques for Parkinson's disease diagnosis is …
[HTML][HTML] Predicting metabolic syndrome with machine learning models using a decision tree algorithm: Retrospective cohort study
CS Yu, YJ Lin, CH Lin, ST Wang, SY Lin… - JMIR medical …, 2020 - medinform.jmir.org
Background: Metabolic syndrome is a cluster of disorders that significantly influence the
development and deterioration of numerous diseases. FibroScan is an ultrasound device …
development and deterioration of numerous diseases. FibroScan is an ultrasound device …
[HTML][HTML] A deep neural network based model for the prediction of hybrid electric vehicles carbon dioxide emissions
Hybrid electric vehicles (HEV) are nowadays proving to be one of the most promising
technologies for the improvement of the fuel economy of several transportation segments. As …
technologies for the improvement of the fuel economy of several transportation segments. As …
A hybrid data mining model for diagnosis of patients with clinical suspicion of dementia
LB Moreira, AA Namen - Computer methods and programs in biomedicine, 2018 - Elsevier
Abstract Background and Objective Given the phenomenon of aging population, dementias
arise as a complex health problem throughout the world. Several methods of machine …
arise as a complex health problem throughout the world. Several methods of machine …
PPAD: a deep learning architecture to predict progression of Alzheimer's disease
Motivation Alzheimer's disease (AD) is a neurodegenerative disease that affects millions of
people worldwide. Mild cognitive impairment (MCI) is an intermediary stage between …
people worldwide. Mild cognitive impairment (MCI) is an intermediary stage between …
Detection of Parkinson disease in brain MRI using convolutional neural network
Parkinson Disease (PD) is one of the most critical progressive neurological diseases which
mainly affects the motor system. The accurate diagnosis of PD has been a challenge to date …
mainly affects the motor system. The accurate diagnosis of PD has been a challenge to date …