  Background and objectives: Parkinson's disease is a neurological disorder
that affects the motor system producing lack of coordination, resting tremor,
and rigidity. Impairments in handwriting are among the main symptoms of the
disease. Handwriting analysis can help in supporting the diagnosis and in
monitoring the progress of the disease. This paper aims to evaluate the
importance of different groups of features to model handwriting deficits that
appear due to Parkinson's disease; and how those features are able to
discriminate between Parkinson's disease patients and healthy subjects.
  Methods: Features based on kinematic, geometrical and non-linear dynamics
analyses were evaluated to classify Parkinson's disease and healthy subjects.
Classifiers based on K-nearest neighbors, support vector machines, and random
forest were considered.
  Results: Accuracies of up to $93.1\%$ were obtained in the classification of
patients and healthy control subjects. A relevance analysis of the features
indicated that those related to speed, acceleration, and pressure are the most
discriminant. The automatic classification of patients in different stages of
the disease shows $\kappa$ indexes between $0.36$ and $0.44$. Accuracies of up
to $83.3\%$ were obtained in a different dataset used only for validation
purposes.
  Conclusions: The results confirmed the negative impact of aging in the
classification process when we considered different groups of healthy subjects.
In addition, the results reported with the separate validation set comprise a
step towards the development of automated tools to support the diagnosis
process in clinical practice.
