Please use this identifier to cite or link to this item:
Title: A reliable method to predict parkinsons disease stage and progression based on handwriting and re-sampling approaches
Authors: Taleb, Catherine
Likforman-Sulem, Laurence
Khachab, Maha 
Mokbel, Chafic 
Affiliations: Faculty of Medicine 
Department of Electrical Engineering 
Keywords: Support Vector Machine (SVM)
Task analysis
Medical diagnostic imaging
Subjects: Disease
Issue Date: 2018
Publisher: IEEE
Part of: 2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR)
Start page: 7
End page: 12
Conference: IEEE International Workshop on Arabic Script Analysis and Recognition (ASAR), 2018 (2nd : 12-14 March 2018 : London, United Kingdom) 
A reliable system depending on algorithms that assist in the decision-making process to diagnose Parkinsons disease (PD) at an early stage and to predict the Hoehn & Yahr (H&Y) stage and the unified Parkinsons disease rating scale (UPDRS) score is developed. In a previous work [3], we used features extracted from Arabic handwriting for diagnosing PD as binary decision. In this work, we use these features for constructing a prediction model that evaluates the H&Y stage and the UPDRS scores. A multi-class support vector machine (SVM) classifier is trained using re-sampling approaches such as adaptive synthetic sampling approach (ADASYN). The classifier is evaluated with 4-fold cross validation. The experiments show that H&Y stage, UPDRS scores, and total UPDRS can be predicted with accuracies of 94%, 92%, and 88% respectively. The proposed method can be implemented as an efficient clinical decision support system for early detection and monitoring the progression of PD.
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Faculty of Medicine

Show full item record

Record view(s)

checked on Aug 13, 2022

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.