arXiv AI June 4, 2026 By Afshan Hashmi

Early Detection of Alzheimer's Disease Using Explainable Machine Learning on Clinical Biomarkers: A Multi-Class Classification Study Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset

arXiv:2606. 03995v1 Announce Type: cross Abstract: Background: Alzheimer's disease (AD) affects over 55 million people worldwide.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

multimodal

arXiv AI

Jul 3

Predicting Early Stages Of Alzheimer's Disease And Identifying Key Biomarkers Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies

arXiv:2607. 02142v1 Announce Type: cross Abstract: Alzheimers disease (AD) is a brain disorder that develops slowly and mainly affects memory, thinking, language, and daily activities.

By Debopriya Ghosh

arXiv AI

Aug 3

Technological Advances in Detecting and Managing Cognitive Impairment in Older Adults: Trends, Challenges, and Future Directions

arXiv:2607. 28687v1 Announce Type: cross Abstract: As populations age, cognitive decline from mild cognitive impairment (MCI) to dementia is a defining health challenge of the coming decades, yet routine assessment often misses its earliest signs.

By Mohammad Asif, Azizuddin Khan, Mohd Azam, Anurag Rajkumar Bombarde

llms multimodal safety

arXiv Machine Learning

Jun 19

Alzheimer's Disease Diagnosis using a Multimodal Approach with 3D MRI and PET

arXiv:2606. 20037v1 Announce Type: new Abstract: Alzheimer's disease (AD) is an irreversible neurodegenerative disorder and a leading cause of death worldwide.

By Loukas Ilias, Anthi-Maria Vozinaki, Christos Ntanos, Dimitris Askounis

multimodal safety

arXiv Machine Learning

Jul 28

Dementia classification from spontaneous speech using wrapper-based feature selection

arXiv:2502. 03484v3 Announce Type: replace-cross Abstract: Dementia encompasses a group of syndromes that impair cognitive functions such as memory, reasoning, and the ability to perform daily activities.

By Marko Niemel\"a, Mikaela von Bonsdorff, Sami \"Ayr\"am\"o, Tommi K\"arkk\"ainen

arXiv Machine Learning

Jul 14

Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts

arXiv:2607. 11656v1 Announce Type: cross Abstract: Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of real-world clinical data.

By Christelle Schneuwly Diaz, Narmina Baghirova, Duy-Thanh Vu, Duy-Cat Can, Gilles Allali, Philippe Ryvlin, Oliver Y. Ch\'en

llms multimodal safety

arXiv AI

Jun 11

Multimodal Ordinal Modeling of Alzheimer's Disease Severity Using Structural MRI and Clinical Data

arXiv:2606. 11794v1 Announce Type: cross Abstract: Neurodegenerative diseases such as Alzheimer's disease (AD) require accurate and scalable tools for assessing disease severity, yet current clinical staging remains time-intensive and prone to variability.

By Boris-Stephan Rauchmann, Jonathan Laib, Buse Ercik, Robert Perneczky, Sergio Altares-L\'opez

multimodal

Related stories

Predicting Early Stages Of Alzheimer's Disease And Identifying Key Biomarkers Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies

Technological Advances in Detecting and Managing Cognitive Impairment in Older Adults: Trends, Challenges, and Future Directions

Alzheimer's Disease Diagnosis using a Multimodal Approach with 3D MRI and PET

Dementia classification from spontaneous speech using wrapper-based feature selection

Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts

Multimodal Ordinal Modeling of Alzheimer's Disease Severity Using Structural MRI and Clinical Data