Abstract:
Parkinson’s disease is a severe neurodegenerative disorder that impairs the
motor system over time, causing the slowness of speech and movements, as well
as abnormal writing abilities due to tremors. Parkinson’s patients are not suitable
for all types of PD diagnosis tests due to their physical problems. As a result,
a handwriting test can be used to construct an automated diagnostic tool as a
potential marker. While traditional techniques focused on the effectiveness of online
and offline or combining both features of handwriting from established templates
characterizing the presence and absence of PD. In this study, we use the PaHaW
dataset to carry out a comprehensive study to assess the optimal set of features that
are more informative as a function of the templates from which they are extracted.
For this purpose, We extract online and offline features subjects,combined extracted
features and employed a feature selection mechanism , such as a genetic algorithm
and correlation, to find the most relevant features that describe the presence and
absence of PD by achieving an overall accuracy of 77.46% .
