Classification of Parkinson’s Disease Using Dynamic Time Warping

Deteriorations in handwriting or in basic shape sketching are one of the most referenced symptoms for early diagnosis of Parkinson's disease (PD). For this reason, the design of a fair, trustworthy and efficacious Computer-aided Diagnosis (CAD) model has supportive importance for the early diagnosis of PD. In this study we investigate the effectiveness of Dynamic Time Warping (DTW) algorithm, which is applied to Archimedean spiral drawings of patients with PD and healthy controls (HC), on PD and healthy subject classification problem. Leave-one-subject-out (LOSO) cross validation scheme is used while training and testing in support vector machine (SVM) and k-nearest neighbors (k-NN) classifiers with various parameters. The accuracy results of% 94.44 (% 95.83) and % 97.52 (% 94.44) are achieved by k-NN and SVM classifiers respectively for static (dynamic) spiral test.