Feature Selection of Arabic Online Handwriting Using Recursive Feature Elimination for Parkinson’s Disease Diagnosis

Parkinson’s disease (PD) is one of the most common neurodegenerative diseases affecting a large population worldwide. Parkinson’s disease is characterized by rigidity, slowness of movement and tremors at rest, these syndromes are frequently manifested in the deterioration of handwriting. The aim of...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:E3S web of conferences Ročník 351; s. 1044
Hlavní autoři: Amakrane, Meryem, Khaissidi, Ghizlane, Mrabti, Mostafa, Ammour, Alae, Faouzi, Belahsen, Aboulem, Ghita
Médium: Journal Article Konferenční příspěvek
Jazyk:angličtina
Vydáno: Les Ulis EDP Sciences 2022
Témata:
Classifiers
Copying
Decision trees
Handwriting
Kernels
Movement disorders
Neurodegenerative diseases
Parkinson's disease
Performance measurement
Rigidity
Support vector machines
ISSN:2267-1242, 2555-0403, 2267-1242
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Parkinson’s disease (PD) is one of the most common neurodegenerative diseases affecting a large population worldwide. Parkinson’s disease is characterized by rigidity, slowness of movement and tremors at rest, these syndromes are frequently manifested in the deterioration of handwriting. The aim of this article is to perform online Arabic handwriting analysis for two types of tasks, TASK 1: copying arabic imposed text and TASK 2: writing arabic desired text. A novel method of handwriting selection features is proposed to obtain the relevant features to efficiently identify subjects with PD, based on Recursive Feature Elimination with Cross-Validation (RFECV), three different RFE estimators were compared: Support Vector Machine, Decision Trees and Random Forest, the selected features have been fed to the same classifiers above to determine the best classifier for predicting Parkinson’s disease. Result: An accuracy of 94.4% was obtained using SVM with Linear kernel, based on 55 features selected using RFE-SVM(Linear) for TASK 1, for TASK 2 an accuracy of 93.7% was obtained using SVM with RBF kernel, based only in 7 features selected using RFE-SVM(Linear). For all the classifiers used, this technique experimentally demonstrates an increase in performance metrics.
Bibliografie:ObjectType-Conference Proceeding-1
SourceType-Conference Papers & Proceedings-1
content type line 21
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/202235101044