@inproceedings{8674be2e57c64b219adef327e7b1f9be,
title = "A Reliable Method to Predict Parkinson's Disease Stage and Progression based on Handwriting and Re-sampling Approaches",
abstract = "A reliable system depending on algorithms that assist in the decision-making process to diagnose Parkinson's disease (PD) at an early stage and to predict the Hoehn Yahr (HY) stage and the unified Parkinson's 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 HY 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 HY 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.",
keywords = "ADASYN, CV, H\&Y, PDMultiMC dataset, Parkinson's disease (PD), SVM, UPDRS, handwriting",
author = "Catherine Taleb and Maha Khachab and Chafic Mokbel and Laurence Likforman-Sulem",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018 ; Conference date: 12-03-2018 Through 14-03-2018",
year = "2018",
month = oct,
day = "2",
doi = "10.1109/ASAR.2018.8480209",
language = "English",
series = "2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "7--12",
booktitle = "2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018",
}