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|Title:||A reliable method to predict parkinsons disease stage and progression based on handwriting and re-sampling approaches||Authors:||Taleb, Catherine
|Affiliations:||Faculty of Medicine
Department of Electrical Engineering
|Keywords:||Support Vector Machine (SVM)
Medical diagnostic imaging
|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 , 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|
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