  Alzheimer's disease is one of the most incisive illnesses among the
neurodegenerative ones, and it causes a progressive decline in cognitive
abilities that, in the worst cases, becomes severe enough to interfere with
daily life. Currently, there is no cure, so an early diagnosis is strongly
needed to try and slow its progression through medical treatments. Handwriting
analysis is considered a potential tool for detecting and understanding certain
neurological conditions, including Alzheimer's disease. While handwriting
analysis alone cannot provide a definitive diagnosis of Alzheimer's, it may
offer some insights and be used for a comprehensive assessment. The
Sigma-lognormal model is conceived for movement analysis and can also be
applied to handwriting. This model returns a set of lognormal parameters as
output, which forms the basis for the computation of novel and significant
features. This paper presents a machine learning approach applied to
handwriting features extracted through the sigma-lognormal model. The aim is to
develop a support system to help doctors in the diagnosis and study of
Alzheimer, evaluate the effectiveness of the extracted features and finally
study the relation among them.
