Updrs-Based Machine Learning in Staging Of parkinson’s Disease

The Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) is a widely accepted tool for assessing Parkinson’s disease (PD) severity and staging. However, manual assessment is subjective and may lack consistency. Therefore, this study proposes developing a machine learning (ML)-based model for PD staging using MDS-UPDRS features to categorize patients into three stages: normal, mild, and severe. To achieve this, five ML classification algorithms– Decision Tree, Subspace Discriminant Ensemble Classifiers, Support Vector Machine (SVM), Kernel naive Bayes, and K-Nearest Neighbors (KNN) — were applied to evaluate the performance of MDS-UPDRS Parts I, II, III, and IV in distinguishing between PD and control subjects. The SVM model demonstrated a high classification accuracy of 98.47% with the training dataset while achieving an accuracy of 100% with the test dataset. The results suggest that ML techniques can serve as reliable and efficient tools for PD classification, reducing diagnostic uncertainty and aiding clinical decision-making. These models have the potential to be integrated into healthcare systems as intelligent diagnostic tools for early and automated PD prediction, thereby facilitating timely interventions and improving patient management.