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Communication Dans Un Congrès Année : 2018

A Reliable Method to Predict Parkinson’s Disease Stage and Progression based on Handwriting and Re-sampling Approaches

Résumé

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 (H&Y) 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 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.
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Dates et versions

hal-04277497 , version 1 (09-11-2023)

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Catherine Taleb, Maha Khachab, Chafic Mokbel, Laurence Likforman-Sulem. A Reliable Method to Predict Parkinson’s Disease Stage and Progression based on Handwriting and Re-sampling Approaches. 2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR), Mar 2018, London, United Kingdom. pp.7-12, ⟨10.1109/ASAR.2018.8480209⟩. ⟨hal-04277497⟩
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