Over 55 million people worldwide are affected by Alzheimer's disease (AD) related and
other types of dementia; a number expected to double over the next few decades. A slowly
progressing disease, AD damages irreparably brain multiple regions. Lifestyle choices,
age, gender and genetics are risk factors, however its cause is still unknown, and only by
early diagnosis and appropriate therapy can the disease be slowed down. Diagnosis of AD
includes clinical examination, behavioral and psychiatric evaluation and the detection of
specific biomarkers related to AD pathology: amyloid plaques, neurofibrillary tangles and
neuronal injury. The A/T/N (Amyloid/Tau/Neurodegeneration) is used to map these
biomarkers to the classification of the pathology. Neuroimaging techniques can detect the
pathopsysiologic and topographic changes in the brain, and especially magnetic resonance
imaging (MRI) is a commonly used, non invasive, easily accessible examination that
detects changes in the volume, size, shape and texture of the different brain areas and
depicts the progression and type of dementia. Machine learning (ML) is applied widely in
medical application and the neuroimaging field for classification, outlier detection,
regression and clustering problems, using labeled data in supervised models and unlabeled
data in unsupervised ones. ML models used on MRI data can detect healthy versus AD
subjects and predict the course of the disease by detecting changes in the brain. Datasets
need to be examined, preprocessed and transformed before being fed to an ML model, and
those choices often affect the performance and predictions. Data is split into two or three
datasets and fed into the ML models chosen by the researches, then the results are
evaluated using popular metrics, compared and visualized.