Machine learning techniques for the diagnosis of Alzheimer's disease: A review

M Tanveer, B Richhariya, RU Khan… - ACM Transactions on …, 2020 - dl.acm.org
Alzheimer's disease is an incurable neurodegenerative disease primarily affecting the
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

C Prakash, R Kumar, N Mittal - Artificial Intelligence Review, 2018 - Springer
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 …

[HTML][HTML] Advancements in computer-assisted diagnosis of Alzheimer's disease: A comprehensive survey of neuroimaging methods and AI techniques for early …

K Shanmugavadivel, VE Sathishkumar, J Cho… - Ageing Research …, 2023 - Elsevier
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 …

Imaging and machine learning techniques for diagnosis of Alzheimer's disease

G Mirzaei, A Adeli, H Adeli - Reviews in the Neurosciences, 2016 - degruyter.com
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 …

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 …

[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 …

[HTML][HTML] A deep neural network based model for the prediction of hybrid electric vehicles carbon dioxide emissions

C Maino, D Misul, A Di Mauro, E Spessa - Energy and AI, 2021 - Elsevier
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 …

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 …

PPAD: a deep learning architecture to predict progression of Alzheimer's disease

M Al Olaimat, J Martinez, F Saeed, S Bozdag… - …, 2023 - academic.oup.com
Motivation Alzheimer's disease (AD) is a neurodegenerative disease that affects millions of
people worldwide. Mild cognitive impairment (MCI) is an intermediary stage between …

Detection of Parkinson disease in brain MRI using convolutional neural network

PM Shah, A Zeb, U Shafi, SFA Zaidi… - … on automation and …, 2018 - ieeexplore.ieee.org
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 …