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Dynamic Handwriting Analysis for Supporting Earlier Parkinson's Disease Diagnosis.
- Source :
- Information (2078-2489); Oct2018, Vol. 9 Issue 10, p247, 1p
- Publication Year :
- 2018
-
Abstract
- Machine learning techniques are tailored to build intelligent systems to support clinicians at the point of care. In particular, they can complement standard clinical evaluations for the assessment of early signs and manifestations of Parkinson's disease (PD). Patients suffering from PD typically exhibit impairments of previously learned motor skills, such as handwriting. Therefore, handwriting can be considered a powerful marker to develop automatized diagnostic tools. In this paper, we investigated if and to which extent dynamic features of the handwriting process can support PD diagnosis at earlier stages. To this end, a subset of the publicly available PaHaW dataset has been used, including those patients showing only early to mild degree of disease severity. We developed a classification framework based on different classifiers and an ensemble scheme. Some encouraging results have been obtained; in particular, good specificity performances have been observed. This indicates that a handwriting-based decision support tool could be used to administer screening tests useful for ruling in disease. [ABSTRACT FROM AUTHOR]
- Subjects :
- MACHINE learning
PARKINSON'S disease
PATIENTS
AUTOGRAPHS
ROUTINE diagnostic tests
Subjects
Details
- Language :
- English
- ISSN :
- 20782489
- Volume :
- 9
- Issue :
- 10
- Database :
- Complementary Index
- Journal :
- Information (2078-2489)
- Publication Type :
- Academic Journal
- Accession number :
- 132632183
- Full Text :
- https://doi.org/10.3390/info9100247