Alzheimer's disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions
Alzheimer's Disease (AD) is the most prevalent and rapidly progressing neurodegenerative
disorder among the elderly and is a leading cause of dementia. AD results in significant …
disorder among the elderly and is a leading cause of dementia. AD results in significant …
Hypergraph convolutional network for longitudinal data analysis in Alzheimer's disease
Alzheimer's disease (AD) is an irreversible and progressive neurodegenerative disease.
Longitudinal structural magnetic resonance imaging (sMRI) data have been widely used for …
Longitudinal structural magnetic resonance imaging (sMRI) data have been widely used for …
A local spline regression-based framework for semi-supervised sparse feature selection
R Sheikhpour - Knowledge-Based Systems, 2023 - Elsevier
Feature selection (FS) is extensively applied in many machine learning applications for the
selection of relevant features from data sets. A lot of unlabeled data are available in a variety …
selection of relevant features from data sets. A lot of unlabeled data are available in a variety …
Dual hypergraphs with feature weighted and latent space learning for the diagnosis of Alzheimer's disease
In recent years of research on the diagnosis of Alzheimer's disease, capturing data
relationships can help improve model performance. However, the simple graph structure …
relationships can help improve model performance. However, the simple graph structure …
Sparse low-redundancy multilabel feature selection based on dynamic local structure preservation and triple graphs exploration
Much semantic information is involved in multilabel data due to more than one label
associated with each instance. The redundant features and noise challenge knowledge …
associated with each instance. The redundant features and noise challenge knowledge …
Semisupervised Bacterial Heuristic Feature Selection Algorithm for High‐Dimensional Classification with Missing Labels
Feature selection is a crucial method for discovering relevant features in high‐dimensional
data. However, most studies primarily focus on completely labeled data, ignoring the …
data. However, most studies primarily focus on completely labeled data, ignoring the …
Shared Manifold Regularized Joint Feature Selection for Joint Classification and Regression in Alzheimer's Disease Diagnosis
Z Chen, Y Liu, Y Zhang, J Zhu, Q Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
In Alzheimer's disease (AD) diagnosis, joint feature selection for predicting disease labels
(classification) and estimating cognitive scores (regression) with neuroimaging data has …
(classification) and estimating cognitive scores (regression) with neuroimaging data has …
Enhanced Multimodal Low-rank Embedding based Feature Selection Model for Multimodal Alzheimer's Disease Diagnosis
Z Chen, Y Liu, Y Zhang, J Zhu, Q Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Identification of Alzheimer's disease (AD) with multimodal neuroimaging data has been
receiving increasing attention. However, the presence of numerous redundant features and …
receiving increasing attention. However, the presence of numerous redundant features and …
Mining Alzheimer's disease clinical data: reducing effects of natural aging for predicting progression and identifying subtypes
T Han, Y Peng, Y Du, Y Li, Y Wang, W Sun… - Frontiers in …, 2024 - frontiersin.org
Introduction Because Alzheimer's disease (AD) has significant heterogeneity in
encephalatrophy and clinical manifestations, AD research faces two critical challenges …
encephalatrophy and clinical manifestations, AD research faces two critical challenges …