Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current
statistics and future predictions. Despite this, no effective treatment or accurate diagnosis …

There is an increasing role for biological markers (biomarkers) in the understanding and
diagnosis of neurodegenerative disorders. The application of imaging biomarkers …

Recent studies on brain imaging analysis witnessed the core roles of machine learning
techniques in computer-assisted intervention for brain disease diagnosis. Of various …

Both normal ageing and neurodegenerative diseases cause morphological changes to the
brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally …

Research on decision support applications in healthcare, such as those related to diagnosis,
prediction, treatment planning, etc., has seen strongly growing interest in recent years. This …

A large number of papers have introduced novel machine learning and feature extraction
methods for automatic classification of Alzheimer's disease (AD). However, while the vast …

Deep learning (DL) has emerged as a powerful tool to make accurate predictions from
complex data such as image, text, or video. However, its ability to predict phenotypic values …

Abstract Machine learning is increasingly being applied to neuroimaging data. However,
most machine learning algorithms have not been designed to accommodate neuroimaging …

Abstract Machine learning techniques provide new methods to predict diagnosis and clinical
outcomes at an individual level. We aim to review the existing literature on the use of …

Abstract Rey's Auditory Verbal Learning Test (RAVLT) is a powerful neuropsychological tool
for testing episodic memory, which is widely used for the cognitive assessment in dementia …