Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/789
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
Kinematics
Medical diagnostic imaging
Subjects: Disease
Training
Databases
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) 
Abstract: 
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.
URI: https://scholarhub.balamand.edu.lb/handle/uob/789
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Faculty of Medicine

Show full item record

Record view(s)

30
checked on Aug 13, 2022

Google ScholarTM

Check


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